resNet2Test1(test&train).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/09aryan/MEME_CLASSIFICATION_USING_SENTIMENT_ANALYSIS/blob/main/resNet2Test1(test%26train).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HqtFod8f7nJL",
        "outputId": "53246d23-67c6-40db-c04d-e3150b15bfdc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PRa-d8lNWR6P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.metrics import confusion_matrix, classification_report, f1_score, precision_score, recall_score\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the combined dataset\n",
        "df = pd.read_csv('/content/drive/MyDrive/combined_data.csv')\n",
        "\n",
        "# Preprocess the sentiment labels for multi-class classification\n",
        "le = LabelEncoder()\n",
        "df['overall_sentiment'] = le.fit_transform(df['overall_sentiment'])\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(np.vstack(df['Features'].apply(eval).values), df['overall_sentiment'].values, test_size=0.2, random_state=42)\n",
        "\n",
        "# Feature scaling\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Build a sentiment analysis model for multi-class classification\n",
        "num_classes = len(le.classes_)\n",
        "model = Sequential([\n",
        "    Dense(512, activation='relu', input_dim=X_train_scaled.shape[1]),\n",
        "    Dropout(0.5),\n",
        "    Dense(256, activation='relu'),\n",
        "    Dropout(0.3),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dropout(0.2),\n",
        "    Dense(num_classes, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer=Adam(learning_rate=0.00001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train_scaled, y_train, epochs=50, batch_size=100, validation_split=0.2)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = model.evaluate(X_test_scaled, y_test)[1]\n",
        "print(f\"Test Accuracy: {accuracy * 100:.2f}%\")\n",
        "\n",
        "# Predictions on the test set\n",
        "y_pred_prob = model.predict(X_test_scaled)\n",
        "y_pred = np.argmax(y_pred_prob, axis=1)\n",
        "\n",
        "# Confusion Matrix\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Plot Confusion Matrix\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='g', cmap='Blues', xticklabels=le.classes_, yticklabels=le.classes_)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "# Classification Report\n",
        "class_report = classification_report(y_test, y_pred, target_names=le.classes_)\n",
        "print(\"Classification Report:\")\n",
        "print(class_report)\n",
        "\n",
        "# F1 Score\n",
        "f1 = f1_score(y_test, y_pred, average='weighted')\n",
        "print(f\"F1 Score: {f1:.4f}\")\n",
        "\n",
        "# Precision\n",
        "precision = precision_score(y_test, y_pred, average='weighted')\n",
        "print(f\"Precision: {precision:.4f}\")\n",
        "\n",
        "# Recall\n",
        "recall = recall_score(y_test, y_pred, average='weighted')\n",
        "print(f\"Recall: {recall:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tAO03nBx8C6y",
        "outputId": "edded453-2641-440c-e5f5-f529c86f484d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "45/45 [==============================] - 2s 9ms/step - loss: 1.6000 - accuracy: 0.2525 - val_loss: 1.5922 - val_accuracy: 0.3453\n",
            "Epoch 2/50\n",
            "45/45 [==============================] - 0s 6ms/step - loss: 1.5862 - accuracy: 0.3243 - val_loss: 1.5783 - val_accuracy: 0.4350\n",
            "Epoch 3/50\n",
            "45/45 [==============================] - 0s 6ms/step - loss: 1.5715 - accuracy: 0.3954 - val_loss: 1.5635 - val_accuracy: 0.4350\n",
            "Epoch 4/50\n",
            "45/45 [==============================] - 0s 6ms/step - loss: 1.5564 - accuracy: 0.4115 - val_loss: 1.5488 - val_accuracy: 0.4350\n",
            "Epoch 5/50\n",
            "45/45 [==============================] - 0s 5ms/step - loss: 1.5415 - accuracy: 0.4259 - val_loss: 1.5335 - val_accuracy: 0.4350\n",
            "Epoch 6/50\n",
            "45/45 [==============================] - 0s 6ms/step - loss: 1.5248 - accuracy: 0.4382 - val_loss: 1.5179 - val_accuracy: 0.4350\n",
            "Epoch 7/50\n",
            "45/45 [==============================] - 0s 6ms/step - loss: 1.5085 - accuracy: 0.4380 - val_loss: 1.5028 - val_accuracy: 0.4350\n",
            "Epoch 8/50\n",
            "45/45 [==============================] - 0s 5ms/step - loss: 1.4921 - accuracy: 0.4414 - val_loss: 1.4871 - val_accuracy: 0.4350\n",
            "Epoch 9/50\n",
            "45/45 [==============================] - 0s 5ms/step - loss: 1.4807 - accuracy: 0.4353 - val_loss: 1.4729 - val_accuracy: 0.4350\n",
            "Epoch 10/50\n",
            "45/45 [==============================] - 0s 6ms/step - loss: 1.4634 - accuracy: 0.4400 - val_loss: 1.4593 - val_accuracy: 0.4350\n",
            "Epoch 11/50\n",
            "45/45 [==============================] - 0s 7ms/step - loss: 1.4505 - accuracy: 0.4405 - val_loss: 1.4464 - val_accuracy: 0.4350\n",
            "Epoch 12/50\n",
            "45/45 [==============================] - 0s 5ms/step - loss: 1.4371 - accuracy: 0.4431 - val_loss: 1.4348 - val_accuracy: 0.4350\n",
            "Epoch 13/50\n",
            "45/45 [==============================] - 0s 5ms/step - loss: 1.4245 - accuracy: 0.4396 - val_loss: 1.4240 - val_accuracy: 0.4350\n",
            "Epoch 14/50\n",
            "45/45 [==============================] - 0s 6ms/step - loss: 1.4157 - accuracy: 0.4407 - val_loss: 1.4142 - val_accuracy: 0.4350\n",
            "Epoch 15/50\n",
            "45/45 [==============================] - 0s 11ms/step - loss: 1.4049 - accuracy: 0.4409 - val_loss: 1.4053 - val_accuracy: 0.4350\n",
            "Epoch 16/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3929 - accuracy: 0.4418 - val_loss: 1.3969 - val_accuracy: 0.4350\n",
            "Epoch 17/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3873 - accuracy: 0.4427 - val_loss: 1.3897 - val_accuracy: 0.4350\n",
            "Epoch 18/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3807 - accuracy: 0.4378 - val_loss: 1.3834 - val_accuracy: 0.4350\n",
            "Epoch 19/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3711 - accuracy: 0.4398 - val_loss: 1.3776 - val_accuracy: 0.4350\n",
            "Epoch 20/50\n",
            "45/45 [==============================] - 1s 18ms/step - loss: 1.3674 - accuracy: 0.4405 - val_loss: 1.3729 - val_accuracy: 0.4350\n",
            "Epoch 21/50\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 1.3592 - accuracy: 0.4434 - val_loss: 1.3684 - val_accuracy: 0.4350\n",
            "Epoch 22/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3554 - accuracy: 0.4402 - val_loss: 1.3645 - val_accuracy: 0.4350\n",
            "Epoch 23/50\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 1.3516 - accuracy: 0.4375 - val_loss: 1.3610 - val_accuracy: 0.4350\n",
            "Epoch 24/50\n",
            "45/45 [==============================] - 0s 11ms/step - loss: 1.3484 - accuracy: 0.4407 - val_loss: 1.3580 - val_accuracy: 0.4350\n",
            "Epoch 25/50\n",
            "45/45 [==============================] - 0s 11ms/step - loss: 1.3477 - accuracy: 0.4418 - val_loss: 1.3555 - val_accuracy: 0.4350\n",
            "Epoch 26/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3381 - accuracy: 0.4362 - val_loss: 1.3530 - val_accuracy: 0.4350\n",
            "Epoch 27/50\n",
            "45/45 [==============================] - 1s 14ms/step - loss: 1.3395 - accuracy: 0.4391 - val_loss: 1.3507 - val_accuracy: 0.4350\n",
            "Epoch 28/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3380 - accuracy: 0.4331 - val_loss: 1.3488 - val_accuracy: 0.4350\n",
            "Epoch 29/50\n",
            "45/45 [==============================] - 1s 11ms/step - loss: 1.3360 - accuracy: 0.4340 - val_loss: 1.3470 - val_accuracy: 0.4350\n",
            "Epoch 30/50\n",
            "45/45 [==============================] - 0s 11ms/step - loss: 1.3345 - accuracy: 0.4337 - val_loss: 1.3452 - val_accuracy: 0.4350\n",
            "Epoch 31/50\n",
            "45/45 [==============================] - 1s 15ms/step - loss: 1.3299 - accuracy: 0.4331 - val_loss: 1.3437 - val_accuracy: 0.4350\n",
            "Epoch 32/50\n",
            "45/45 [==============================] - 1s 17ms/step - loss: 1.3293 - accuracy: 0.4378 - val_loss: 1.3421 - val_accuracy: 0.4350\n",
            "Epoch 33/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3272 - accuracy: 0.4357 - val_loss: 1.3408 - val_accuracy: 0.4350\n",
            "Epoch 34/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3237 - accuracy: 0.4389 - val_loss: 1.3394 - val_accuracy: 0.4350\n",
            "Epoch 35/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3263 - accuracy: 0.4283 - val_loss: 1.3380 - val_accuracy: 0.4350\n",
            "Epoch 36/50\n",
            "45/45 [==============================] - 0s 10ms/step - loss: 1.3251 - accuracy: 0.4360 - val_loss: 1.3368 - val_accuracy: 0.4350\n",
            "Epoch 37/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3193 - accuracy: 0.4342 - val_loss: 1.3355 - val_accuracy: 0.4350\n",
            "Epoch 38/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3213 - accuracy: 0.4333 - val_loss: 1.3342 - val_accuracy: 0.4350\n",
            "Epoch 39/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3226 - accuracy: 0.4304 - val_loss: 1.3329 - val_accuracy: 0.4350\n",
            "Epoch 40/50\n",
            "45/45 [==============================] - 0s 10ms/step - loss: 1.3178 - accuracy: 0.4306 - val_loss: 1.3318 - val_accuracy: 0.4350\n",
            "Epoch 41/50\n",
            "45/45 [==============================] - 0s 10ms/step - loss: 1.3198 - accuracy: 0.4306 - val_loss: 1.3309 - val_accuracy: 0.4350\n",
            "Epoch 42/50\n",
            "45/45 [==============================] - 0s 7ms/step - loss: 1.3152 - accuracy: 0.4357 - val_loss: 1.3297 - val_accuracy: 0.4350\n",
            "Epoch 43/50\n",
            "45/45 [==============================] - 1s 11ms/step - loss: 1.3163 - accuracy: 0.4306 - val_loss: 1.3287 - val_accuracy: 0.4350\n",
            "Epoch 44/50\n",
            "45/45 [==============================] - 0s 7ms/step - loss: 1.3152 - accuracy: 0.4342 - val_loss: 1.3276 - val_accuracy: 0.4350\n",
            "Epoch 45/50\n",
            "45/45 [==============================] - 0s 10ms/step - loss: 1.3111 - accuracy: 0.4349 - val_loss: 1.3266 - val_accuracy: 0.4350\n",
            "Epoch 46/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3132 - accuracy: 0.4405 - val_loss: 1.3257 - val_accuracy: 0.4350\n",
            "Epoch 47/50\n",
            "45/45 [==============================] - 0s 8ms/step - loss: 1.3137 - accuracy: 0.4371 - val_loss: 1.3250 - val_accuracy: 0.4350\n",
            "Epoch 48/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3096 - accuracy: 0.4335 - val_loss: 1.3241 - val_accuracy: 0.4350\n",
            "Epoch 49/50\n",
            "45/45 [==============================] - 1s 11ms/step - loss: 1.3097 - accuracy: 0.4306 - val_loss: 1.3230 - val_accuracy: 0.4350\n",
            "Epoch 50/50\n",
            "45/45 [==============================] - 0s 9ms/step - loss: 1.3074 - accuracy: 0.4378 - val_loss: 1.3222 - val_accuracy: 0.4350\n",
            "44/44 [==============================] - 0s 7ms/step - loss: 1.2979 - accuracy: 0.4713\n",
            "Test Accuracy: 47.13%\n",
            "44/44 [==============================] - 1s 4ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     negative       0.00      0.00      0.00       103\n",
            "      neutral       0.00      0.00      0.00       405\n",
            "     positive       0.47      1.00      0.64       657\n",
            "very_negative       0.00      0.00      0.00        23\n",
            "very_positive       0.00      0.00      0.00       206\n",
            "\n",
            "     accuracy                           0.47      1394\n",
            "    macro avg       0.09      0.20      0.13      1394\n",
            " weighted avg       0.22      0.47      0.30      1394\n",
            "\n",
            "F1 Score: 0.3019\n",
            "Precision: 0.2221\n",
            "Recall: 0.4713\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Create a Random Forest classifier\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Train the Random Forest model\n",
        "rf_classifier.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Predictions on the test set\n",
        "y_pred_rf = rf_classifier.predict(X_test_scaled)\n",
        "\n",
        "# Evaluate the Random Forest model\n",
        "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
        "print(f\"Random Forest Test Accuracy: {accuracy_rf * 100:.2f}%\")\n",
        "\n",
        "# Confusion Matrix\n",
        "conf_matrix_rf = confusion_matrix(y_test, y_pred_rf)\n",
        "\n",
        "# Plot Confusion Matrix\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(conf_matrix_rf, annot=True, fmt='g', cmap='Blues', xticklabels=le.classes_, yticklabels=le.classes_)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Random Forest Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "# Classification Report\n",
        "class_report_rf = classification_report(y_test, y_pred_rf, target_names=le.classes_)\n",
        "print(\"Random Forest Classification Report:\")\n",
        "print(class_report_rf)\n",
        "\n",
        "# F1 Score\n",
        "f1_rf = f1_score(y_test, y_pred_rf, average='weighted')\n",
        "print(f\"Random Forest F1 Score: {f1_rf:.4f}\")\n",
        "\n",
        "# Precision\n",
        "precision_rf = precision_score(y_test, y_pred_rf, average='weighted')\n",
        "print(f\"Random Forest Precision: {precision_rf:.4f}\")\n",
        "\n",
        "# Recall\n",
        "recall_rf = recall_score(y_test, y_pred_rf, average='weighted')\n",
        "print(f\"Random Forest Recall: {recall_rf:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qKacwIItNnDg",
        "outputId": "8d0881ca-3f5c-401a-d270-4ed6d8cff838"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Test Accuracy: 29.27%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     negative       0.07      0.07      0.07       103\n",
            "      neutral       0.25      0.29      0.27       405\n",
            "     positive       0.43      0.39      0.41       657\n",
            "very_negative       0.00      0.00      0.00        23\n",
            "very_positive       0.13      0.13      0.13       206\n",
            "\n",
            "     accuracy                           0.29      1394\n",
            "    macro avg       0.18      0.18      0.17      1394\n",
            " weighted avg       0.30      0.29      0.30      1394\n",
            "\n",
            "Random Forest F1 Score: 0.2959\n",
            "Random Forest Precision: 0.3008\n",
            "Random Forest Recall: 0.2927\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Dense, Dropout, Input, Embedding, Flatten, concatenate\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Load the combined dataset\n",
        "df = pd.read_csv('/content/drive/MyDrive/combined_data.csv')\n",
        "\n",
        "# Preprocess the sentiment labels for multi-class classification\n",
        "le = LabelEncoder()\n",
        "df['overall_sentiment'] = le.fit_transform(df['overall_sentiment'])\n",
        "\n",
        "# Handle missing values in 'text_corrected'\n",
        "df['text_corrected'].fillna('', inplace=True)\n",
        "\n",
        "# Tokenize the text data\n",
        "max_words = 10000  # Adjust based on your dataset\n",
        "tokenizer = Tokenizer(num_words=max_words, oov_token=\"<OOV>\")\n",
        "tokenizer.fit_on_texts(df['text_corrected'])\n",
        "text_sequences = tokenizer.texts_to_sequences(df['text_corrected'])\n",
        "text_padded = pad_sequences(text_sequences)\n",
        "\n",
        "# Combine InceptionV3 features with text-based features\n",
        "X_text = text_padded\n",
        "X_features = np.vstack(df['Features'].apply(eval).values)\n",
        "X_combined = np.hstack((X_text, X_features))\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_combined, df['overall_sentiment'].values, test_size=0.2, random_state=42)\n",
        "\n",
        "# Feature scaling\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Build a combined model for multi-class classification\n",
        "num_classes = len(le.classes_)\n",
        "\n",
        "# Text branch\n",
        "text_input = Input(shape=(X_text.shape[1],))\n",
        "embedding_layer = Embedding(input_dim=max_words, output_dim=128)(text_input)\n",
        "flatten_layer = Flatten()(embedding_layer)\n",
        "\n",
        "# Features branch\n",
        "features_input = Input(shape=(X_features.shape[1],))\n",
        "\n",
        "# Concatenate both branches\n",
        "merged = concatenate([flatten_layer, features_input])\n",
        "\n",
        "# Dense layers\n",
        "dense_1 = Dense(512, activation='relu')(merged)\n",
        "dropout_1 = Dropout(0.5)(dense_1)\n",
        "dense_2 = Dense(256, activation='relu')(dropout_1)\n",
        "dropout_2 = Dropout(0.3)(dense_2)\n",
        "dense_3 = Dense(128, activation='relu')(dropout_2)\n",
        "dropout_3 = Dropout(0.2)(dense_3)\n",
        "output_layer = Dense(num_classes, activation='softmax')(dropout_3)\n",
        "\n",
        "# Create the model\n",
        "model = Model(inputs=[text_input, features_input], outputs=output_layer)\n",
        "\n",
        "model.compile(optimizer=Adam(learning_rate=0.00001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit([X_train[:, :X_text.shape[1]], X_train[:, X_text.shape[1]:]], y_train, epochs=50, batch_size=100, validation_split=0.2)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = model.evaluate([X_test[:, :X_text.shape[1]], X_test[:, X_text.shape[1]:]], y_test)[1]\n",
        "print(f\"Test Accuracy: {accuracy * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZK9HgRdI9Rzv",
        "outputId": "16050222-8bb8-4bfd-cf66-4815930fd515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "45/45 [==============================] - 29s 576ms/step - loss: 1.4615 - accuracy: 0.3781 - val_loss: 1.3567 - val_accuracy: 0.4350\n",
            "Epoch 2/50\n",
            "45/45 [==============================] - 24s 533ms/step - loss: 1.3391 - accuracy: 0.4109 - val_loss: 1.3217 - val_accuracy: 0.4350\n",
            "Epoch 3/50\n",
            "45/45 [==============================] - 25s 552ms/step - loss: 1.3167 - accuracy: 0.4088 - val_loss: 1.3095 - val_accuracy: 0.4350\n",
            "Epoch 4/50\n",
            "45/45 [==============================] - 23s 513ms/step - loss: 1.3103 - accuracy: 0.4198 - val_loss: 1.3056 - val_accuracy: 0.4350\n",
            "Epoch 5/50\n",
            "45/45 [==============================] - 29s 652ms/step - loss: 1.3079 - accuracy: 0.4151 - val_loss: 1.3059 - val_accuracy: 0.4350\n",
            "Epoch 6/50\n",
            "45/45 [==============================] - 23s 513ms/step - loss: 1.3071 - accuracy: 0.4109 - val_loss: 1.3051 - val_accuracy: 0.4350\n",
            "Epoch 7/50\n",
            "45/45 [==============================] - 26s 588ms/step - loss: 1.3062 - accuracy: 0.4207 - val_loss: 1.3044 - val_accuracy: 0.4350\n",
            "Epoch 8/50\n",
            "45/45 [==============================] - 24s 537ms/step - loss: 1.3049 - accuracy: 0.4241 - val_loss: 1.3023 - val_accuracy: 0.4350\n",
            "Epoch 9/50\n",
            "45/45 [==============================] - 24s 540ms/step - loss: 1.3038 - accuracy: 0.4221 - val_loss: 1.3025 - val_accuracy: 0.4350\n",
            "Epoch 10/50\n",
            "45/45 [==============================] - 23s 509ms/step - loss: 1.3075 - accuracy: 0.4133 - val_loss: 1.3027 - val_accuracy: 0.4350\n",
            "Epoch 11/50\n",
            "45/45 [==============================] - 23s 516ms/step - loss: 1.3053 - accuracy: 0.4160 - val_loss: 1.3027 - val_accuracy: 0.4350\n",
            "Epoch 12/50\n",
            "45/45 [==============================] - 25s 553ms/step - loss: 1.3030 - accuracy: 0.4187 - val_loss: 1.3027 - val_accuracy: 0.4350\n",
            "Epoch 13/50\n",
            "45/45 [==============================] - 23s 515ms/step - loss: 1.3020 - accuracy: 0.4234 - val_loss: 1.3022 - val_accuracy: 0.4350\n",
            "Epoch 14/50\n",
            "45/45 [==============================] - 22s 501ms/step - loss: 1.2984 - accuracy: 0.4113 - val_loss: 1.3023 - val_accuracy: 0.4350\n",
            "Epoch 15/50\n",
            "45/45 [==============================] - 25s 557ms/step - loss: 1.2914 - accuracy: 0.4295 - val_loss: 1.3020 - val_accuracy: 0.4350\n",
            "Epoch 16/50\n",
            "45/45 [==============================] - 23s 517ms/step - loss: 1.2937 - accuracy: 0.4261 - val_loss: 1.3012 - val_accuracy: 0.4350\n",
            "Epoch 17/50\n",
            "45/45 [==============================] - 23s 516ms/step - loss: 1.2990 - accuracy: 0.4212 - val_loss: 1.3009 - val_accuracy: 0.4350\n",
            "Epoch 18/50\n",
            "45/45 [==============================] - 30s 668ms/step - loss: 1.2959 - accuracy: 0.4245 - val_loss: 1.3004 - val_accuracy: 0.4350\n",
            "Epoch 19/50\n",
            "45/45 [==============================] - 28s 624ms/step - loss: 1.2961 - accuracy: 0.4183 - val_loss: 1.3005 - val_accuracy: 0.4350\n",
            "Epoch 20/50\n",
            "45/45 [==============================] - 25s 548ms/step - loss: 1.2923 - accuracy: 0.4245 - val_loss: 1.3003 - val_accuracy: 0.4350\n",
            "Epoch 21/50\n",
            "45/45 [==============================] - 23s 513ms/step - loss: 1.2911 - accuracy: 0.4192 - val_loss: 1.3008 - val_accuracy: 0.4350\n",
            "Epoch 22/50\n",
            "45/45 [==============================] - 22s 497ms/step - loss: 1.2995 - accuracy: 0.4142 - val_loss: 1.3010 - val_accuracy: 0.4350\n",
            "Epoch 23/50\n",
            "45/45 [==============================] - 27s 598ms/step - loss: 1.2959 - accuracy: 0.4214 - val_loss: 1.3012 - val_accuracy: 0.4350\n",
            "Epoch 24/50\n",
            "45/45 [==============================] - 27s 587ms/step - loss: 1.2904 - accuracy: 0.4297 - val_loss: 1.3006 - val_accuracy: 0.4350\n",
            "Epoch 25/50\n",
            "45/45 [==============================] - 25s 550ms/step - loss: 1.2959 - accuracy: 0.4189 - val_loss: 1.3007 - val_accuracy: 0.4350\n",
            "Epoch 26/50\n",
            "45/45 [==============================] - 23s 513ms/step - loss: 1.2964 - accuracy: 0.4270 - val_loss: 1.3007 - val_accuracy: 0.4350\n",
            "Epoch 27/50\n",
            "45/45 [==============================] - 23s 513ms/step - loss: 1.2971 - accuracy: 0.4333 - val_loss: 1.3004 - val_accuracy: 0.4350\n",
            "Epoch 28/50\n",
            "45/45 [==============================] - 25s 554ms/step - loss: 1.2921 - accuracy: 0.4171 - val_loss: 1.3006 - val_accuracy: 0.4350\n",
            "Epoch 29/50\n",
            "45/45 [==============================] - 22s 500ms/step - loss: 1.2905 - accuracy: 0.4232 - val_loss: 1.3002 - val_accuracy: 0.4350\n",
            "Epoch 30/50\n",
            "45/45 [==============================] - 23s 511ms/step - loss: 1.2910 - accuracy: 0.4243 - val_loss: 1.3011 - val_accuracy: 0.4350\n",
            "Epoch 31/50\n",
            "45/45 [==============================] - 24s 537ms/step - loss: 1.2980 - accuracy: 0.4239 - val_loss: 1.3002 - val_accuracy: 0.4350\n",
            "Epoch 32/50\n",
            "45/45 [==============================] - 24s 532ms/step - loss: 1.2875 - accuracy: 0.4326 - val_loss: 1.2998 - val_accuracy: 0.4350\n",
            "Epoch 33/50\n",
            "45/45 [==============================] - 23s 512ms/step - loss: 1.2913 - accuracy: 0.4241 - val_loss: 1.2997 - val_accuracy: 0.4350\n",
            "Epoch 34/50\n",
            "45/45 [==============================] - 24s 529ms/step - loss: 1.2916 - accuracy: 0.4239 - val_loss: 1.2996 - val_accuracy: 0.4350\n",
            "Epoch 35/50\n",
            "45/45 [==============================] - 25s 549ms/step - loss: 1.2851 - accuracy: 0.4349 - val_loss: 1.2992 - val_accuracy: 0.4350\n",
            "Epoch 36/50\n",
            "45/45 [==============================] - 23s 512ms/step - loss: 1.2924 - accuracy: 0.4194 - val_loss: 1.2997 - val_accuracy: 0.4350\n",
            "Epoch 37/50\n",
            "45/45 [==============================] - 23s 514ms/step - loss: 1.2841 - accuracy: 0.4315 - val_loss: 1.2995 - val_accuracy: 0.4350\n",
            "Epoch 38/50\n",
            "45/45 [==============================] - 25s 558ms/step - loss: 1.2874 - accuracy: 0.4245 - val_loss: 1.2992 - val_accuracy: 0.4350\n",
            "Epoch 39/50\n",
            "45/45 [==============================] - 22s 497ms/step - loss: 1.2898 - accuracy: 0.4209 - val_loss: 1.2997 - val_accuracy: 0.4350\n",
            "Epoch 40/50\n",
            "45/45 [==============================] - 26s 588ms/step - loss: 1.2882 - accuracy: 0.4268 - val_loss: 1.2995 - val_accuracy: 0.4350\n",
            "Epoch 41/50\n",
            "45/45 [==============================] - 28s 612ms/step - loss: 1.2880 - accuracy: 0.4243 - val_loss: 1.2986 - val_accuracy: 0.4350\n",
            "Epoch 42/50\n",
            "45/45 [==============================] - 23s 519ms/step - loss: 1.2893 - accuracy: 0.4342 - val_loss: 1.2982 - val_accuracy: 0.4350\n",
            "Epoch 43/50\n",
            "45/45 [==============================] - 23s 512ms/step - loss: 1.2814 - accuracy: 0.4322 - val_loss: 1.2986 - val_accuracy: 0.4350\n",
            "Epoch 44/50\n",
            "45/45 [==============================] - 24s 541ms/step - loss: 1.2798 - accuracy: 0.4342 - val_loss: 1.2988 - val_accuracy: 0.4350\n",
            "Epoch 45/50\n",
            "45/45 [==============================] - 24s 532ms/step - loss: 1.2820 - accuracy: 0.4322 - val_loss: 1.2998 - val_accuracy: 0.4350\n",
            "Epoch 46/50\n",
            "45/45 [==============================] - 22s 495ms/step - loss: 1.2879 - accuracy: 0.4156 - val_loss: 1.2993 - val_accuracy: 0.4350\n",
            "Epoch 47/50\n",
            "45/45 [==============================] - 23s 507ms/step - loss: 1.2860 - accuracy: 0.4286 - val_loss: 1.2986 - val_accuracy: 0.4350\n",
            "Epoch 48/50\n",
            "45/45 [==============================] - 25s 552ms/step - loss: 1.2831 - accuracy: 0.4214 - val_loss: 1.2990 - val_accuracy: 0.4350\n",
            "Epoch 49/50\n",
            "45/45 [==============================] - 23s 510ms/step - loss: 1.2795 - accuracy: 0.4306 - val_loss: 1.2989 - val_accuracy: 0.4350\n",
            "Epoch 50/50\n",
            "45/45 [==============================] - 23s 508ms/step - loss: 1.2775 - accuracy: 0.4214 - val_loss: 1.2980 - val_accuracy: 0.4350\n",
            "44/44 [==============================] - 1s 32ms/step - loss: 1.2663 - accuracy: 0.4713\n",
            "Test Accuracy: 47.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "# Create an MLP classifier\n",
        "mlp_classifier = MLPClassifier(hidden_layer_sizes=(512, 256, 128), max_iter=1000, random_state=42)\n",
        "\n",
        "# Train the MLP model\n",
        "mlp_classifier.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Predictions on the test set\n",
        "y_pred_mlp = mlp_classifier.predict(X_test_scaled)\n",
        "\n",
        "# Evaluate the MLP model\n",
        "accuracy_mlp = accuracy_score(y_test, y_pred_mlp)\n",
        "print(f\"MLP Test Accuracy: {accuracy_mlp * 100:.2f}%\")\n",
        "\n",
        "# Confusion Matrix\n",
        "conf_matrix_mlp = confusion_matrix(y_test, y_pred_mlp)\n",
        "\n",
        "# Plot Confusion Matrix\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(conf_matrix_mlp, annot=True, fmt='g', cmap='Blues', xticklabels=le.classes_, yticklabels=le.classes_)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('MLP Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "# Classification Report\n",
        "class_report_mlp = classification_report(y_test, y_pred_mlp, target_names=le.classes_)\n",
        "print(\"MLP Classification Report:\")\n",
        "print(class_report_mlp)\n",
        "\n",
        "# F1 Score\n",
        "f1_mlp = f1_score(y_test, y_pred_mlp, average='weighted')\n",
        "print(f\"MLP F1 Score: {f1_mlp:.4f}\")\n",
        "\n",
        "# Precision\n",
        "precision_mlp = precision_score(y_test, y_pred_mlp, average='weighted')\n",
        "print(f\"MLP Precision: {precision_mlp:.4f}\")\n",
        "\n",
        "# Recall\n",
        "recall_mlp = recall_score(y_test, y_pred_mlp, average='weighted')\n",
        "print(f\"MLP Recall: {recall_mlp:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7YutUW4f_Mgl",
        "outputId": "bfd0fdb1-0d1f-497a-8edd-bc9cd08e149d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MLP Test Accuracy: 47.13%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MLP Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     negative       0.00      0.00      0.00       103\n",
            "      neutral       0.00      0.00      0.00       405\n",
            "     positive       0.47      1.00      0.64       657\n",
            "very_negative       0.00      0.00      0.00        23\n",
            "very_positive       0.00      0.00      0.00       206\n",
            "\n",
            "     accuracy                           0.47      1394\n",
            "    macro avg       0.09      0.20      0.13      1394\n",
            " weighted avg       0.22      0.47      0.30      1394\n",
            "\n",
            "MLP F1 Score: 0.3019\n",
            "MLP Precision: 0.2221\n",
            "MLP Recall: 0.4713\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import (\n",
        "    Dense,\n",
        "    Dropout,\n",
        "    Input,\n",
        "    Embedding,\n",
        "    Flatten,\n",
        "    concatenate,\n",
        ")\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# Load the combined dataset\n",
        "df = pd.read_csv('/content/drive/MyDrive/combined_data.csv')\n",
        "\n",
        "# Preprocess the sentiment labels for multi-class classification\n",
        "le = LabelEncoder()\n",
        "df['overall_sentiment'] = le.fit_transform(df['overall_sentiment'])\n",
        "\n",
        "# Handle missing values in 'text_corrected'\n",
        "df['text_corrected'].fillna('', inplace=True)\n",
        "\n",
        "# Tokenize the text data\n",
        "max_words = 10000\n",
        "tokenizer = Tokenizer(num_words=max_words, oov_token=\"<OOV>\")\n",
        "tokenizer.fit_on_texts(df['text_corrected'])\n",
        "text_sequences = tokenizer.texts_to_sequences(df['text_corrected'])\n",
        "text_padded = pad_sequences(text_sequences)\n",
        "\n",
        "# Combine InceptionV3 features with text-based features\n",
        "X_text = text_padded\n",
        "X_features = np.vstack(df['Features'].apply(eval).values)\n",
        "X_combined = np.hstack((X_text, X_features))\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X_combined, df['overall_sentiment'].values, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "# Feature scaling\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Data augmentation for text data\n",
        "datagen_text = ImageDataGenerator(\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        ")\n",
        "\n",
        "# Build a combined model for multi-class classification\n",
        "num_classes = len(le.classes_)\n",
        "\n",
        "# Text branch\n",
        "text_input = Input(shape=(X_text.shape[1],))\n",
        "embedding_layer = Embedding(input_dim=max_words, output_dim=128)(text_input)\n",
        "flatten_layer = Flatten()(embedding_layer)\n",
        "\n",
        "# Features branch\n",
        "features_input = Input(shape=(X_features.shape[1],))\n",
        "\n",
        "# Concatenate both branches\n",
        "merged = concatenate([flatten_layer, features_input])\n",
        "\n",
        "# Dense layers with increased complexity\n",
        "dense_1 = Dense(512, activation='relu')(merged)\n",
        "dropout_1 = Dropout(0.5)(dense_1)\n",
        "dense_2 = Dense(256, activation='relu')(dropout_1)\n",
        "dropout_2 = Dropout(0.3)(dense_2)\n",
        "dense_3 = Dense(128, activation='relu')(dropout_2)\n",
        "dropout_3 = Dropout(0.2)(dense_3)\n",
        "output_layer = Dense(num_classes, activation='softmax')(dropout_3)\n",
        "\n",
        "# Create the model\n",
        "model = Model(inputs=[text_input, features_input], outputs=output_layer)\n",
        "\n",
        "model.compile(\n",
        "    optimizer=Adam(learning_rate=0.0001),\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy'],\n",
        ")\n",
        "\n",
        "# Early stopping\n",
        "early_stopping = EarlyStopping(\n",
        "    monitor='val_loss', patience=10, restore_best_weights=True\n",
        ")\n",
        "\n",
        "# Train the model with data augmentation\n",
        "history = model.fit(\n",
        "    [X_train[:, : X_text.shape[1]], X_train[:, X_text.shape[1]:]],\n",
        "    y_train,\n",
        "    epochs=100,\n",
        "    batch_size=64,  # Experiment with batch size\n",
        "    validation_split=0.2,\n",
        "    callbacks=[early_stopping],\n",
        ")\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = model.evaluate(\n",
        "    [X_test[:, : X_text.shape[1]], X_test[:, X_text.shape[1]:]], y_test\n",
        ")[1]\n",
        "print(f\"Test Accuracy: {accuracy * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0UMt-yyGBf9",
        "outputId": "7d1e380a-5a03-4e10-f89d-c62b8dbae438"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "70/70 [==============================] - 38s 515ms/step - loss: 1.3420 - accuracy: 0.4079 - val_loss: 1.3087 - val_accuracy: 0.4350\n",
            "Epoch 2/100\n",
            "70/70 [==============================] - 33s 470ms/step - loss: 1.3018 - accuracy: 0.4209 - val_loss: 1.3005 - val_accuracy: 0.4350\n",
            "Epoch 3/100\n",
            "70/70 [==============================] - 30s 424ms/step - loss: 1.2996 - accuracy: 0.4207 - val_loss: 1.3022 - val_accuracy: 0.4350\n",
            "Epoch 4/100\n",
            "70/70 [==============================] - 32s 459ms/step - loss: 1.2956 - accuracy: 0.4279 - val_loss: 1.2993 - val_accuracy: 0.4350\n",
            "Epoch 5/100\n",
            "70/70 [==============================] - 32s 465ms/step - loss: 1.2886 - accuracy: 0.4297 - val_loss: 1.3002 - val_accuracy: 0.4350\n",
            "Epoch 6/100\n",
            "70/70 [==============================] - 33s 468ms/step - loss: 1.2876 - accuracy: 0.4333 - val_loss: 1.3050 - val_accuracy: 0.4350\n",
            "Epoch 7/100\n",
            "70/70 [==============================] - 31s 444ms/step - loss: 1.2871 - accuracy: 0.4288 - val_loss: 1.2979 - val_accuracy: 0.4350\n",
            "Epoch 8/100\n",
            "70/70 [==============================] - 31s 438ms/step - loss: 1.2863 - accuracy: 0.4324 - val_loss: 1.3030 - val_accuracy: 0.4350\n",
            "Epoch 9/100\n",
            "70/70 [==============================] - 32s 465ms/step - loss: 1.2765 - accuracy: 0.4328 - val_loss: 1.3048 - val_accuracy: 0.4350\n",
            "Epoch 10/100\n",
            "70/70 [==============================] - 33s 469ms/step - loss: 1.2779 - accuracy: 0.4333 - val_loss: 1.3040 - val_accuracy: 0.4350\n",
            "Epoch 11/100\n",
            "70/70 [==============================] - 32s 457ms/step - loss: 1.2765 - accuracy: 0.4346 - val_loss: 1.2984 - val_accuracy: 0.4350\n",
            "Epoch 12/100\n",
            "70/70 [==============================] - 30s 433ms/step - loss: 1.2676 - accuracy: 0.4373 - val_loss: 1.3059 - val_accuracy: 0.4350\n",
            "Epoch 13/100\n",
            "70/70 [==============================] - 39s 558ms/step - loss: 1.2595 - accuracy: 0.4351 - val_loss: 1.2981 - val_accuracy: 0.4350\n",
            "Epoch 14/100\n",
            "70/70 [==============================] - 32s 461ms/step - loss: 1.2429 - accuracy: 0.4353 - val_loss: 1.3053 - val_accuracy: 0.4350\n",
            "Epoch 15/100\n",
            "70/70 [==============================] - 32s 458ms/step - loss: 1.2243 - accuracy: 0.4429 - val_loss: 1.3030 - val_accuracy: 0.4350\n",
            "Epoch 16/100\n",
            "70/70 [==============================] - 31s 434ms/step - loss: 1.1832 - accuracy: 0.4409 - val_loss: 1.3061 - val_accuracy: 0.4350\n",
            "Epoch 17/100\n",
            "70/70 [==============================] - 33s 462ms/step - loss: 1.1248 - accuracy: 0.4423 - val_loss: 1.3200 - val_accuracy: 0.4350\n",
            "44/44 [==============================] - 1s 32ms/step - loss: 1.2624 - accuracy: 0.4713\n",
            "Test Accuracy: 47.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the CSV files into pandas DataFrames\n",
        "labels_df = pd.read_csv('/content/drive/MyDrive/labels.csv')\n",
        "image_data_df = pd.read_csv('/content/drive/MyDrive/image_data_fine_tuned.csv')\n",
        "\n",
        "# Merge the DataFrames based on the 'image_name' and 'ImageName' columns\n",
        "combined_df = pd.merge(labels_df, image_data_df, left_on='image_name', right_on='ImageName', how='inner')\n",
        "\n",
        "# Select only the columns of interest\n",
        "result_df = combined_df[['image_name', 'text_ocr', 'text_corrected', 'overall_sentiment', 'Features']]\n",
        "\n",
        "# Save the result to a new CSV file\n",
        "result_df.to_csv('/content/drive/MyDrive/combined_data2.csv', index=False)\n"
      ],
      "metadata": {
        "id": "2hJA0LFOQhsP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "# Load the combined dataset\n",
        "df = pd.read_csv('/content/drive/MyDrive/combined_data2.csv')\n",
        "\n",
        "# Preprocess the sentiment labels for multi-class classification\n",
        "le = LabelEncoder()\n",
        "df['overall_sentiment'] = le.fit_transform(df['overall_sentiment'])\n",
        "\n",
        "# Split the dataset into features (X) and labels (y)\n",
        "X = np.vstack(df['Features'].apply(eval).values)\n",
        "y = df['overall_sentiment'].values\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Feature scaling\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Build a sentiment analysis model for multi-class classification\n",
        "num_classes = len(le.classes_)\n",
        "model = Sequential([\n",
        "    Dense(512, activation='relu', input_dim=X_train_scaled.shape[1]),\n",
        "    Dropout(0.5),\n",
        "    Dense(256, activation='relu'),\n",
        "    Dropout(0.3),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dropout(0.2),\n",
        "    Dense(num_classes, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer=Adam(learning_rate=0.00001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Convert labels to one-hot encoding for multi-class classification\n",
        "y_train_one_hot = to_categorical(y_train, num_classes)\n",
        "y_test_one_hot = to_categorical(y_test, num_classes)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train_scaled, y_train_one_hot, epochs=50, batch_size=100, validation_split=0.2)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = model.evaluate(X_test_scaled, y_test_one_hot)[1]\n",
        "print(f\"Test Accuracy: {accuracy * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48T2bCBbQzMw",
        "outputId": "ec0d548e-2459-41bb-ac88-6c8bea02e6ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "45/45 [==============================] - 3s 43ms/step - loss: 1.5937 - accuracy: 0.3068 - val_loss: 1.5913 - val_accuracy: 0.4350\n",
            "Epoch 2/50\n",
            "45/45 [==============================] - 1s 28ms/step - loss: 1.5828 - accuracy: 0.3653 - val_loss: 1.5802 - val_accuracy: 0.4350\n",
            "Epoch 3/50\n",
            "45/45 [==============================] - 1s 20ms/step - loss: 1.5716 - accuracy: 0.4122 - val_loss: 1.5690 - val_accuracy: 0.4350\n",
            "Epoch 4/50\n",
            "45/45 [==============================] - 1s 20ms/step - loss: 1.5593 - accuracy: 0.4277 - val_loss: 1.5571 - val_accuracy: 0.4350\n",
            "Epoch 5/50\n",
            "45/45 [==============================] - 1s 19ms/step - loss: 1.5472 - accuracy: 0.4351 - val_loss: 1.5442 - val_accuracy: 0.4350\n",
            "Epoch 6/50\n",
            "45/45 [==============================] - 1s 19ms/step - loss: 1.5329 - accuracy: 0.4389 - val_loss: 1.5304 - val_accuracy: 0.4350\n",
            "Epoch 7/50\n",
            "45/45 [==============================] - 1s 20ms/step - loss: 1.5187 - accuracy: 0.4355 - val_loss: 1.5165 - val_accuracy: 0.4350\n",
            "Epoch 8/50\n",
            "45/45 [==============================] - 1s 15ms/step - loss: 1.5074 - accuracy: 0.4382 - val_loss: 1.5022 - val_accuracy: 0.4350\n",
            "Epoch 9/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.4918 - accuracy: 0.4400 - val_loss: 1.4882 - val_accuracy: 0.4350\n",
            "Epoch 10/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.4744 - accuracy: 0.4420 - val_loss: 1.4744 - val_accuracy: 0.4350\n",
            "Epoch 11/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.4641 - accuracy: 0.4387 - val_loss: 1.4609 - val_accuracy: 0.4350\n",
            "Epoch 12/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.4489 - accuracy: 0.4405 - val_loss: 1.4480 - val_accuracy: 0.4350\n",
            "Epoch 13/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.4363 - accuracy: 0.4414 - val_loss: 1.4361 - val_accuracy: 0.4350\n",
            "Epoch 14/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.4259 - accuracy: 0.4427 - val_loss: 1.4248 - val_accuracy: 0.4350\n",
            "Epoch 15/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.4120 - accuracy: 0.4411 - val_loss: 1.4143 - val_accuracy: 0.4350\n",
            "Epoch 16/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.4017 - accuracy: 0.4416 - val_loss: 1.4045 - val_accuracy: 0.4350\n",
            "Epoch 17/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3926 - accuracy: 0.4425 - val_loss: 1.3959 - val_accuracy: 0.4350\n",
            "Epoch 18/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3869 - accuracy: 0.4416 - val_loss: 1.3883 - val_accuracy: 0.4350\n",
            "Epoch 19/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3795 - accuracy: 0.4409 - val_loss: 1.3817 - val_accuracy: 0.4350\n",
            "Epoch 20/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3717 - accuracy: 0.4396 - val_loss: 1.3758 - val_accuracy: 0.4350\n",
            "Epoch 21/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3688 - accuracy: 0.4434 - val_loss: 1.3706 - val_accuracy: 0.4350\n",
            "Epoch 22/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3631 - accuracy: 0.4396 - val_loss: 1.3659 - val_accuracy: 0.4350\n",
            "Epoch 23/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3585 - accuracy: 0.4416 - val_loss: 1.3618 - val_accuracy: 0.4350\n",
            "Epoch 24/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3551 - accuracy: 0.4378 - val_loss: 1.3581 - val_accuracy: 0.4350\n",
            "Epoch 25/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3511 - accuracy: 0.4378 - val_loss: 1.3549 - val_accuracy: 0.4350\n",
            "Epoch 26/50\n",
            "45/45 [==============================] - 1s 18ms/step - loss: 1.3456 - accuracy: 0.4384 - val_loss: 1.3516 - val_accuracy: 0.4350\n",
            "Epoch 27/50\n",
            "45/45 [==============================] - 1s 18ms/step - loss: 1.3447 - accuracy: 0.4369 - val_loss: 1.3487 - val_accuracy: 0.4350\n",
            "Epoch 28/50\n",
            "45/45 [==============================] - 1s 20ms/step - loss: 1.3414 - accuracy: 0.4427 - val_loss: 1.3462 - val_accuracy: 0.4350\n",
            "Epoch 29/50\n",
            "45/45 [==============================] - 1s 20ms/step - loss: 1.3389 - accuracy: 0.4371 - val_loss: 1.3437 - val_accuracy: 0.4350\n",
            "Epoch 30/50\n",
            "45/45 [==============================] - 1s 18ms/step - loss: 1.3340 - accuracy: 0.4362 - val_loss: 1.3415 - val_accuracy: 0.4350\n",
            "Epoch 31/50\n",
            "45/45 [==============================] - 1s 17ms/step - loss: 1.3328 - accuracy: 0.4396 - val_loss: 1.3394 - val_accuracy: 0.4350\n",
            "Epoch 32/50\n",
            "45/45 [==============================] - 1s 17ms/step - loss: 1.3354 - accuracy: 0.4387 - val_loss: 1.3373 - val_accuracy: 0.4350\n",
            "Epoch 33/50\n",
            "45/45 [==============================] - 1s 19ms/step - loss: 1.3320 - accuracy: 0.4364 - val_loss: 1.3354 - val_accuracy: 0.4350\n",
            "Epoch 34/50\n",
            "45/45 [==============================] - 1s 20ms/step - loss: 1.3287 - accuracy: 0.4389 - val_loss: 1.3337 - val_accuracy: 0.4350\n",
            "Epoch 35/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3299 - accuracy: 0.4364 - val_loss: 1.3320 - val_accuracy: 0.4350\n",
            "Epoch 36/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3250 - accuracy: 0.4400 - val_loss: 1.3303 - val_accuracy: 0.4350\n",
            "Epoch 37/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3258 - accuracy: 0.4351 - val_loss: 1.3289 - val_accuracy: 0.4350\n",
            "Epoch 38/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3207 - accuracy: 0.4335 - val_loss: 1.3274 - val_accuracy: 0.4350\n",
            "Epoch 39/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3194 - accuracy: 0.4393 - val_loss: 1.3260 - val_accuracy: 0.4350\n",
            "Epoch 40/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3186 - accuracy: 0.4346 - val_loss: 1.3247 - val_accuracy: 0.4350\n",
            "Epoch 41/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3163 - accuracy: 0.4371 - val_loss: 1.3234 - val_accuracy: 0.4350\n",
            "Epoch 42/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3148 - accuracy: 0.4342 - val_loss: 1.3222 - val_accuracy: 0.4350\n",
            "Epoch 43/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3145 - accuracy: 0.4349 - val_loss: 1.3210 - val_accuracy: 0.4350\n",
            "Epoch 44/50\n",
            "45/45 [==============================] - 1s 13ms/step - loss: 1.3133 - accuracy: 0.4362 - val_loss: 1.3199 - val_accuracy: 0.4350\n",
            "Epoch 45/50\n",
            "45/45 [==============================] - 1s 11ms/step - loss: 1.3102 - accuracy: 0.4353 - val_loss: 1.3188 - val_accuracy: 0.4350\n",
            "Epoch 46/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3079 - accuracy: 0.4362 - val_loss: 1.3178 - val_accuracy: 0.4350\n",
            "Epoch 47/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3111 - accuracy: 0.4366 - val_loss: 1.3168 - val_accuracy: 0.4350\n",
            "Epoch 48/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3045 - accuracy: 0.4380 - val_loss: 1.3160 - val_accuracy: 0.4350\n",
            "Epoch 49/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3042 - accuracy: 0.4409 - val_loss: 1.3150 - val_accuracy: 0.4350\n",
            "Epoch 50/50\n",
            "45/45 [==============================] - 1s 12ms/step - loss: 1.3046 - accuracy: 0.4351 - val_loss: 1.3142 - val_accuracy: 0.4350\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 1.2894 - accuracy: 0.4713\n",
            "Test Accuracy: 47.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas tensorflow transformers scikit-learn\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_dzGweyWjcS",
        "outputId": "627a4a84-93af-4060-bc04-76601de4aeb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.14.0)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.3.post1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.23.5)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.5.26)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.5.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.9.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (16.0.6)\n",
            "Requirement already satisfied: ml-dtypes==0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (67.7.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.3.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.5.0)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.34.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.59.2)\n",
            "Requirement already satisfied: tensorboard<2.15,>=2.14 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.14.1)\n",
            "Requirement already satisfied: tensorflow-estimator<2.15,>=2.14.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.14.0)\n",
            "Requirement already satisfied: keras<2.15,>=2.14.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.14.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.17.3)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.15,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.14.1)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.3)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.2.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.41.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.17.3)\n",
            "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (1.0.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (3.5.1)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (3.0.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (5.3.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (1.3.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.15,>=2.14->tensorflow) (2.1.3)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Input, concatenate\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# Load your dataset\n",
        "# df = pd.read_csv('/path/to/your/meme_dataset.csv')\n",
        "\n",
        "# Preprocess the sentiment labels\n",
        "le = LabelEncoder()\n",
        "df['label'] = le.fit_transform(df['overall_sentiment'])\n",
        "\n",
        "# Fill NaN values in the 'text_corrected' column with an empty string\n",
        "df['text_corrected'].fillna('', inplace=True)\n",
        "\n",
        "# Frequency encode the text features\n",
        "vectorizer = CountVectorizer()\n",
        "text_features = vectorizer.fit_transform(df['text_corrected']).toarray()\n",
        "\n",
        "# Concatenate text features with numerical features\n",
        "combined_features = np.hstack((text_features, np.vstack(df['Features'].apply(eval).values)))\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(combined_features, df['label'].values, test_size=0.2, random_state=42)\n",
        "\n",
        "# Build a neural network model\n",
        "model = Sequential([\n",
        "    Input(shape=(combined_features.shape[1],)),\n",
        "    Dense(512, activation='relu'),\n",
        "    Dense(256, activation='relu'),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(len(le.classes_), activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer=Adam(learning_rate=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
        "\n",
        "# Evaluate the model\n",
        "test_accuracy = model.evaluate(X_test, y_test)[1]\n",
        "print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VLC4CsuqWUK-",
        "outputId": "22efa19b-7e02-4fbe-fec7-cba5298e209d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "140/140 [==============================] - 23s 155ms/step - loss: 1.3208 - accuracy: 0.4353 - val_loss: 1.3293 - val_accuracy: 0.3220\n",
            "Epoch 2/10\n",
            "140/140 [==============================] - 18s 130ms/step - loss: 0.9682 - accuracy: 0.5898 - val_loss: 1.6018 - val_accuracy: 0.3614\n",
            "Epoch 3/10\n",
            "140/140 [==============================] - 18s 131ms/step - loss: 0.3210 - accuracy: 0.8906 - val_loss: 2.4049 - val_accuracy: 0.3372\n",
            "Epoch 4/10\n",
            "140/140 [==============================] - 18s 130ms/step - loss: 0.0902 - accuracy: 0.9751 - val_loss: 3.0705 - val_accuracy: 0.3345\n",
            "Epoch 5/10\n",
            "140/140 [==============================] - 22s 161ms/step - loss: 0.0544 - accuracy: 0.9850 - val_loss: 2.9245 - val_accuracy: 0.3291\n",
            "Epoch 6/10\n",
            "140/140 [==============================] - 18s 127ms/step - loss: 0.0248 - accuracy: 0.9917 - val_loss: 3.4630 - val_accuracy: 0.3408\n",
            "Epoch 7/10\n",
            "140/140 [==============================] - 18s 128ms/step - loss: 0.0162 - accuracy: 0.9942 - val_loss: 3.9009 - val_accuracy: 0.3596\n",
            "Epoch 8/10\n",
            "140/140 [==============================] - 18s 130ms/step - loss: 0.0212 - accuracy: 0.9930 - val_loss: 3.7432 - val_accuracy: 0.3300\n",
            "Epoch 9/10\n",
            "140/140 [==============================] - 18s 132ms/step - loss: 0.0178 - accuracy: 0.9930 - val_loss: 3.6209 - val_accuracy: 0.3641\n",
            "Epoch 10/10\n",
            "140/140 [==============================] - 25s 180ms/step - loss: 0.0133 - accuracy: 0.9933 - val_loss: 4.0041 - val_accuracy: 0.3578\n",
            "44/44 [==============================] - 1s 27ms/step - loss: 3.6484 - accuracy: 0.3838\n",
            "Test Accuracy: 38.38%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_accuracy = history.history['accuracy'][-1]  # Get the last accuracy from the training history\n",
        "\n",
        "print(f\"Train Accuracy: {train_accuracy * 100:.2f}%\")\n",
        "print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yyYOeKIBldRC",
        "outputId": "ccc17add-47cd-45ea-cf6e-d36ada4fa36c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 44.23%\n",
            "Test Accuracy: 38.38%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Input, concatenate\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# Load your dataset\n",
        "# df = pd.read_csv('/path/to/your/meme_dataset.csv')\n",
        "\n",
        "# Preprocess the sentiment labels\n",
        "le = LabelEncoder()\n",
        "df['label'] = le.fit_transform(df['overall_sentiment'])\n",
        "\n",
        "# Fill NaN values in the 'text_corrected' column with an empty string\n",
        "df['text_corrected'].fillna('', inplace=True)\n",
        "\n",
        "# Frequency encode the text features\n",
        "vectorizer = CountVectorizer()\n",
        "text_features = vectorizer.fit_transform(df['text_corrected']).toarray()\n",
        "\n",
        "# Concatenate text features with numerical features\n",
        "combined_features = np.hstack((text_features, np.vstack(df['Features'].apply(eval).values)))\n",
        "\n",
        "# Convert labels to numpy array\n",
        "labels = df['label'].values\n",
        "\n",
        "# Number of folds\n",
        "k_folds = 5\n",
        "skf = StratifiedKFold(n_splits=k_folds, shuffle=True, random_state=42)\n",
        "\n",
        "# Loop over folds\n",
        "for fold, (train_index, val_index) in enumerate(skf.split(combined_features, labels)):\n",
        "    print(f\"\\nFold {fold + 1}/{k_folds}\")\n",
        "\n",
        "    # Split data into train and validation sets\n",
        "    X_train, X_val = combined_features[train_index], combined_features[val_index]\n",
        "    y_train, y_val = labels[train_index], labels[val_index]\n",
        "\n",
        "    # Build a neural network model\n",
        "    model = Sequential([\n",
        "        Input(shape=(combined_features.shape[1],)),\n",
        "        Dense(512, activation='relu'),\n",
        "        Dense(256, activation='relu'),\n",
        "        Dense(128, activation='relu'),\n",
        "        Dense(len(le.classes_), activation='softmax')\n",
        "    ])\n",
        "\n",
        "    model.compile(optimizer=Adam(learning_rate=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "    # Train the model\n",
        "    model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))\n",
        "\n",
        "    # Evaluate the model on the validation set\n",
        "    val_loss, val_accuracy = model.evaluate(X_val, y_val)\n",
        "    print(f\"Validation Accuracy: {val_accuracy * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBRH7l9rl4NA",
        "outputId": "2d9af45e-99c3-40ce-eb25-3885862f52a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Fold 1/5\n",
            "Epoch 1/10\n",
            "175/175 [==============================] - 30s 166ms/step - loss: 1.3151 - accuracy: 0.4444 - val_loss: 1.2887 - val_accuracy: 0.4462\n",
            "Epoch 2/10\n",
            "175/175 [==============================] - 26s 147ms/step - loss: 0.9993 - accuracy: 0.5655 - val_loss: 1.5271 - val_accuracy: 0.3723\n",
            "Epoch 3/10\n",
            "175/175 [==============================] - 25s 145ms/step - loss: 0.3721 - accuracy: 0.8744 - val_loss: 2.1575 - val_accuracy: 0.3558\n",
            "Epoch 4/10\n",
            "175/175 [==============================] - 23s 133ms/step - loss: 0.1147 - accuracy: 0.9684 - val_loss: 2.8204 - val_accuracy: 0.3874\n",
            "Epoch 5/10\n",
            "175/175 [==============================] - 27s 153ms/step - loss: 0.0622 - accuracy: 0.9822 - val_loss: 2.8361 - val_accuracy: 0.3515\n",
            "Epoch 6/10\n",
            "175/175 [==============================] - 26s 148ms/step - loss: 0.0387 - accuracy: 0.9869 - val_loss: 3.1291 - val_accuracy: 0.3565\n",
            "Epoch 7/10\n",
            "175/175 [==============================] - 26s 146ms/step - loss: 0.0286 - accuracy: 0.9891 - val_loss: 3.3312 - val_accuracy: 0.3780\n",
            "Epoch 8/10\n",
            "175/175 [==============================] - 28s 161ms/step - loss: 0.0237 - accuracy: 0.9901 - val_loss: 3.7690 - val_accuracy: 0.3458\n",
            "Epoch 9/10\n",
            "175/175 [==============================] - 24s 139ms/step - loss: 0.0219 - accuracy: 0.9887 - val_loss: 3.7667 - val_accuracy: 0.3651\n",
            "Epoch 10/10\n",
            "175/175 [==============================] - 26s 147ms/step - loss: 0.0196 - accuracy: 0.9894 - val_loss: 3.9145 - val_accuracy: 0.3709\n",
            "44/44 [==============================] - 1s 22ms/step - loss: 3.9145 - accuracy: 0.3709\n",
            "Validation Accuracy: 37.09%\n",
            "\n",
            "Fold 2/5\n",
            "Epoch 1/10\n",
            "175/175 [==============================] - 29s 161ms/step - loss: 1.3232 - accuracy: 0.4419 - val_loss: 1.2991 - val_accuracy: 0.4469\n",
            "Epoch 2/10\n",
            "175/175 [==============================] - 30s 172ms/step - loss: 0.9859 - accuracy: 0.5870 - val_loss: 1.5096 - val_accuracy: 0.3666\n",
            "Epoch 3/10\n",
            "175/175 [==============================] - 35s 202ms/step - loss: 0.3789 - accuracy: 0.8732 - val_loss: 2.1483 - val_accuracy: 0.3321\n",
            "Epoch 4/10\n",
            "175/175 [==============================] - 26s 150ms/step - loss: 0.1397 - accuracy: 0.9596 - val_loss: 2.7135 - val_accuracy: 0.3537\n",
            "Epoch 5/10\n",
            "175/175 [==============================] - 31s 179ms/step - loss: 0.0649 - accuracy: 0.9821 - val_loss: 2.9091 - val_accuracy: 0.3601\n",
            "Epoch 6/10\n",
            "175/175 [==============================] - 28s 161ms/step - loss: 0.0418 - accuracy: 0.9853 - val_loss: 3.1116 - val_accuracy: 0.3407\n",
            "Epoch 7/10\n",
            "175/175 [==============================] - 27s 152ms/step - loss: 0.0298 - accuracy: 0.9887 - val_loss: 3.3712 - val_accuracy: 0.3451\n",
            "Epoch 8/10\n",
            "175/175 [==============================] - 27s 152ms/step - loss: 0.0233 - accuracy: 0.9891 - val_loss: 3.6375 - val_accuracy: 0.3372\n",
            "Epoch 9/10\n",
            "175/175 [==============================] - 29s 163ms/step - loss: 0.0241 - accuracy: 0.9887 - val_loss: 3.5767 - val_accuracy: 0.3580\n",
            "Epoch 10/10\n",
            "175/175 [==============================] - 25s 143ms/step - loss: 0.0180 - accuracy: 0.9903 - val_loss: 3.9398 - val_accuracy: 0.3228\n",
            "44/44 [==============================] - 1s 33ms/step - loss: 3.9398 - accuracy: 0.3228\n",
            "Validation Accuracy: 32.28%\n",
            "\n",
            "Fold 3/5\n",
            "Epoch 1/10\n",
            "175/175 [==============================] - 27s 149ms/step - loss: 1.3172 - accuracy: 0.4325 - val_loss: 1.3442 - val_accuracy: 0.3436\n",
            "Epoch 2/10\n",
            "175/175 [==============================] - 28s 162ms/step - loss: 0.9821 - accuracy: 0.5883 - val_loss: 1.5218 - val_accuracy: 0.3981\n",
            "Epoch 3/10\n",
            "175/175 [==============================] - 29s 166ms/step - loss: 0.3568 - accuracy: 0.8827 - val_loss: 2.2579 - val_accuracy: 0.3329\n",
            "Epoch 4/10\n",
            "175/175 [==============================] - 25s 145ms/step - loss: 0.1214 - accuracy: 0.9656 - val_loss: 2.6427 - val_accuracy: 0.3508\n",
            "Epoch 5/10\n",
            "175/175 [==============================] - 26s 147ms/step - loss: 0.0680 - accuracy: 0.9828 - val_loss: 2.7987 - val_accuracy: 0.3558\n",
            "Epoch 6/10\n",
            "175/175 [==============================] - 30s 169ms/step - loss: 0.0452 - accuracy: 0.9858 - val_loss: 3.1000 - val_accuracy: 0.3465\n",
            "Epoch 7/10\n",
            "175/175 [==============================] - 28s 160ms/step - loss: 0.0284 - accuracy: 0.9882 - val_loss: 3.5808 - val_accuracy: 0.3780\n",
            "Epoch 8/10\n",
            "175/175 [==============================] - 26s 150ms/step - loss: 0.0253 - accuracy: 0.9885 - val_loss: 3.5421 - val_accuracy: 0.3465\n",
            "Epoch 9/10\n",
            "175/175 [==============================] - 26s 146ms/step - loss: 0.0191 - accuracy: 0.9901 - val_loss: 3.7605 - val_accuracy: 0.3580\n",
            "Epoch 10/10\n",
            "175/175 [==============================] - 28s 160ms/step - loss: 0.0177 - accuracy: 0.9909 - val_loss: 4.0054 - val_accuracy: 0.3515\n",
            "44/44 [==============================] - 1s 16ms/step - loss: 4.0054 - accuracy: 0.3515\n",
            "Validation Accuracy: 35.15%\n",
            "\n",
            "Fold 4/5\n",
            "Epoch 1/10\n",
            "175/175 [==============================] - 28s 147ms/step - loss: 1.3132 - accuracy: 0.4373 - val_loss: 1.2897 - val_accuracy: 0.4472\n",
            "Epoch 2/10\n",
            "175/175 [==============================] - 25s 142ms/step - loss: 0.9522 - accuracy: 0.6129 - val_loss: 1.5367 - val_accuracy: 0.3568\n",
            "Epoch 3/10\n",
            "175/175 [==============================] - 26s 149ms/step - loss: 0.3151 - accuracy: 0.9039 - val_loss: 2.1329 - val_accuracy: 0.3209\n",
            "Epoch 4/10\n",
            "175/175 [==============================] - 25s 143ms/step - loss: 0.1081 - accuracy: 0.9699 - val_loss: 2.7212 - val_accuracy: 0.3309\n",
            "Epoch 5/10\n",
            "175/175 [==============================] - 24s 138ms/step - loss: 0.0707 - accuracy: 0.9808 - val_loss: 2.7788 - val_accuracy: 0.3683\n",
            "Epoch 6/10\n",
            "175/175 [==============================] - 28s 162ms/step - loss: 0.0391 - accuracy: 0.9873 - val_loss: 3.2123 - val_accuracy: 0.3604\n",
            "Epoch 7/10\n",
            "175/175 [==============================] - 24s 137ms/step - loss: 0.0268 - accuracy: 0.9889 - val_loss: 3.8006 - val_accuracy: 0.3754\n",
            "Epoch 8/10\n",
            "175/175 [==============================] - 27s 152ms/step - loss: 0.0237 - accuracy: 0.9889 - val_loss: 3.7008 - val_accuracy: 0.3726\n",
            "Epoch 9/10\n",
            "175/175 [==============================] - 25s 145ms/step - loss: 0.0190 - accuracy: 0.9887 - val_loss: 3.8577 - val_accuracy: 0.3496\n",
            "Epoch 10/10\n",
            "175/175 [==============================] - 24s 140ms/step - loss: 0.0186 - accuracy: 0.9892 - val_loss: 4.0922 - val_accuracy: 0.3388\n",
            "44/44 [==============================] - 1s 32ms/step - loss: 4.0922 - accuracy: 0.3388\n",
            "Validation Accuracy: 33.88%\n",
            "\n",
            "Fold 5/5\n",
            "Epoch 1/10\n",
            "175/175 [==============================] - 27s 149ms/step - loss: 1.3086 - accuracy: 0.4418 - val_loss: 1.2902 - val_accuracy: 0.4472\n",
            "Epoch 2/10\n",
            "175/175 [==============================] - 27s 153ms/step - loss: 0.9698 - accuracy: 0.5928 - val_loss: 1.6031 - val_accuracy: 0.3869\n",
            "Epoch 3/10\n",
            "175/175 [==============================] - 28s 163ms/step - loss: 0.3250 - accuracy: 0.8911 - val_loss: 2.3376 - val_accuracy: 0.3410\n",
            "Epoch 4/10\n",
            "175/175 [==============================] - 30s 169ms/step - loss: 0.1148 - accuracy: 0.9711 - val_loss: 2.6996 - val_accuracy: 0.3676\n",
            "Epoch 5/10\n",
            "175/175 [==============================] - 26s 151ms/step - loss: 0.0531 - accuracy: 0.9855 - val_loss: 3.0271 - val_accuracy: 0.3331\n",
            "Epoch 6/10\n",
            "175/175 [==============================] - 27s 153ms/step - loss: 0.0350 - accuracy: 0.9882 - val_loss: 3.2751 - val_accuracy: 0.3130\n",
            "Epoch 7/10\n",
            "175/175 [==============================] - 26s 149ms/step - loss: 0.0248 - accuracy: 0.9896 - val_loss: 3.5400 - val_accuracy: 0.3568\n",
            "Epoch 8/10\n",
            "175/175 [==============================] - 30s 170ms/step - loss: 0.0228 - accuracy: 0.9901 - val_loss: 3.5710 - val_accuracy: 0.3676\n",
            "Epoch 9/10\n",
            "175/175 [==============================] - 27s 157ms/step - loss: 0.0166 - accuracy: 0.9914 - val_loss: 3.9578 - val_accuracy: 0.3568\n",
            "Epoch 10/10\n",
            "175/175 [==============================] - 27s 154ms/step - loss: 0.0169 - accuracy: 0.9900 - val_loss: 3.9845 - val_accuracy: 0.3819\n",
            "44/44 [==============================] - 1s 19ms/step - loss: 3.9845 - accuracy: 0.3819\n",
            "Validation Accuracy: 38.19%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Dense, Dropout\n",
        "from sklearn.metrics import accuracy_score\n",
        "from ast import literal_eval\n",
        "\n",
        "# Load the dataset\n",
        "csv_file_path = '/content/drive/MyDrive/combined_data2_mapped.csv'\n",
        "df = pd.read_csv(csv_file_path)\n",
        "\n",
        "# Convert string representations of lists to actual lists\n",
        "df['Features'] = df['Features'].apply(literal_eval)\n",
        "\n",
        "# Extract features\n",
        "X = np.vstack(df['Features'])\n",
        "y_positive = df['positive'].values\n",
        "y_negative = df['negative'].values\n",
        "\n",
        "# Create the new target variable y_neutral\n",
        "y_neutral = np.where((y_positive == 0) & (y_negative == 0), 1, 0)\n",
        "\n",
        "# Standardize the input features\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train_positive, y_test_positive, y_train_negative, y_test_negative, y_train_neutral, y_test_neutral = train_test_split(\n",
        "    X, y_positive, y_negative, y_neutral, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "# Define a simple model\n",
        "def simple_model(input_dim, dropout_rate=0.1):\n",
        "    inputs = Input(shape=(input_dim,))\n",
        "    x = Dense(64, activation='relu')(inputs)\n",
        "    x = Dropout(dropout_rate)(x)\n",
        "    x = Dense(32, activation='relu')(x)\n",
        "    x = Dropout(dropout_rate)(x)\n",
        "    outputs = Dense(1, activation='sigmoid')(x)  # Use sigmoid for binary classification\n",
        "    model = Model(inputs=inputs, outputs=outputs)\n",
        "    return model\n",
        "\n",
        "# Build and compile the positive sentiment model\n",
        "model_positive = simple_model(input_dim=X_train.shape[1])\n",
        "model_positive.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
        "\n",
        "# Train the positive sentiment model\n",
        "model_positive.fit(X_train, y_train_positive, epochs=100, batch_size=2, validation_split=0.2)\n",
        "\n",
        "# Evaluate the positive sentiment model\n",
        "test_pred_positive = model_positive.predict(X_test)\n",
        "test_accuracy_positive = accuracy_score(y_test_positive, (test_pred_positive > 0.5).astype(int))\n",
        "print(f\"Positive Sentiment Model - Test Accuracy: {test_accuracy_positive}\")\n",
        "\n",
        "# Build and compile the negative sentiment model\n",
        "model_negative = simple_model(input_dim=X_train.shape[1])\n",
        "model_negative.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
        "\n",
        "# Train the negative sentiment model\n",
        "model_negative.fit(X_train, y_train_negative, epochs=100, batch_size=2, validation_split=0.2)\n",
        "\n",
        "# Evaluate the negative sentiment model\n",
        "test_pred_negative = model_negative.predict(X_test)\n",
        "test_accuracy_negative = accuracy_score(y_test_negative, (test_pred_negative > 0.5).astype(int))\n",
        "print(f\"Negative Sentiment Model - Test Accuracy: {test_accuracy_negative}\")\n",
        "\n",
        "# Build and compile the neutral sentiment model\n",
        "model_neutral = simple_model(input_dim=X_train.shape[1])\n",
        "model_neutral.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
        "\n",
        "# Train the neutral sentiment model\n",
        "model_neutral.fit(X_train, y_train_neutral, epochs=100, batch_size=2, validation_split=0.2)\n",
        "\n",
        "# Evaluate the neutral sentiment model\n",
        "test_pred_neutral = model_neutral.predict(X_test)\n",
        "test_accuracy_neutral = accuracy_score(y_test_neutral, (test_pred_neutral > 0.5).astype(int))\n",
        "print(f\"Neutral Sentiment Model - Test Accuracy: {test_accuracy_neutral}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DioX7h5INl_x",
        "outputId": "44422582-dd01-4d10-852e-d73084c5cdc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -167.3957 - accuracy: 0.3234 - val_loss: -596.3799 - val_accuracy: 0.3112\n",
            "Epoch 2/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -2319.4468 - accuracy: 0.3234 - val_loss: -4171.0386 - val_accuracy: 0.3112\n",
            "Epoch 3/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -8442.7227 - accuracy: 0.3234 - val_loss: -11658.2852 - val_accuracy: 0.3112\n",
            "Epoch 4/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -19465.8906 - accuracy: 0.3234 - val_loss: -24158.6465 - val_accuracy: 0.3112\n",
            "Epoch 5/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -36451.3633 - accuracy: 0.3234 - val_loss: -42176.5039 - val_accuracy: 0.3112\n",
            "Epoch 6/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -59784.0156 - accuracy: 0.3234 - val_loss: -66818.6328 - val_accuracy: 0.3112\n",
            "Epoch 7/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -91418.1328 - accuracy: 0.3234 - val_loss: -98678.3438 - val_accuracy: 0.3112\n",
            "Epoch 8/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -132833.0781 - accuracy: 0.3234 - val_loss: -139188.5312 - val_accuracy: 0.3112\n",
            "Epoch 9/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -181869.1719 - accuracy: 0.3234 - val_loss: -188841.5625 - val_accuracy: 0.3112\n",
            "Epoch 10/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -241196.6094 - accuracy: 0.3234 - val_loss: -248596.5469 - val_accuracy: 0.3112\n",
            "Epoch 11/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -319291.1875 - accuracy: 0.3234 - val_loss: -320935.9688 - val_accuracy: 0.3112\n",
            "Epoch 12/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -399780.1875 - accuracy: 0.3234 - val_loss: -403483.0312 - val_accuracy: 0.3112\n",
            "Epoch 13/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -502007.9688 - accuracy: 0.3234 - val_loss: -499421.3438 - val_accuracy: 0.3112\n",
            "Epoch 14/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -618757.2500 - accuracy: 0.3234 - val_loss: -609130.4375 - val_accuracy: 0.3112\n",
            "Epoch 15/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -741574.8125 - accuracy: 0.3234 - val_loss: -730716.6875 - val_accuracy: 0.3112\n",
            "Epoch 16/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -892261.7500 - accuracy: 0.3234 - val_loss: -869954.8750 - val_accuracy: 0.3112\n",
            "Epoch 17/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -1056674.7500 - accuracy: 0.3234 - val_loss: -1025533.0000 - val_accuracy: 0.3112\n",
            "Epoch 18/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -1237834.8750 - accuracy: 0.3234 - val_loss: -1198687.0000 - val_accuracy: 0.3112\n",
            "Epoch 19/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -1445997.1250 - accuracy: 0.3234 - val_loss: -1390568.5000 - val_accuracy: 0.3112\n",
            "Epoch 20/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -1673442.1250 - accuracy: 0.3234 - val_loss: -1600286.7500 - val_accuracy: 0.3112\n",
            "Epoch 21/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -1917775.5000 - accuracy: 0.3234 - val_loss: -1827578.5000 - val_accuracy: 0.3112\n",
            "Epoch 22/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -2167983.0000 - accuracy: 0.3234 - val_loss: -2074042.0000 - val_accuracy: 0.3112\n",
            "Epoch 23/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -2477815.7500 - accuracy: 0.3234 - val_loss: -2343249.0000 - val_accuracy: 0.3112\n",
            "Epoch 24/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -2793748.2500 - accuracy: 0.3234 - val_loss: -2633227.0000 - val_accuracy: 0.3112\n",
            "Epoch 25/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -3142136.0000 - accuracy: 0.3234 - val_loss: -2953243.7500 - val_accuracy: 0.3112\n",
            "Epoch 26/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -3498336.5000 - accuracy: 0.3234 - val_loss: -3296310.0000 - val_accuracy: 0.3112\n",
            "Epoch 27/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -3910899.7500 - accuracy: 0.3234 - val_loss: -3659365.5000 - val_accuracy: 0.3112\n",
            "Epoch 28/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -4321018.0000 - accuracy: 0.3234 - val_loss: -4050801.7500 - val_accuracy: 0.3112\n",
            "Epoch 29/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -4756623.0000 - accuracy: 0.3234 - val_loss: -4468596.0000 - val_accuracy: 0.3112\n",
            "Epoch 30/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -5239887.0000 - accuracy: 0.3234 - val_loss: -4913000.5000 - val_accuracy: 0.3112\n",
            "Epoch 31/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -5786735.5000 - accuracy: 0.3234 - val_loss: -5384949.0000 - val_accuracy: 0.3112\n",
            "Epoch 32/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -6307388.0000 - accuracy: 0.3234 - val_loss: -5887352.0000 - val_accuracy: 0.3112\n",
            "Epoch 33/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -6847627.5000 - accuracy: 0.3234 - val_loss: -6413292.5000 - val_accuracy: 0.3112\n",
            "Epoch 34/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -7542265.0000 - accuracy: 0.3234 - val_loss: -6974140.5000 - val_accuracy: 0.3112\n",
            "Epoch 35/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -8085765.0000 - accuracy: 0.3234 - val_loss: -7560403.0000 - val_accuracy: 0.3112\n",
            "Epoch 36/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -8795530.0000 - accuracy: 0.3234 - val_loss: -8179680.0000 - val_accuracy: 0.3112\n",
            "Epoch 37/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -9445585.0000 - accuracy: 0.3234 - val_loss: -8821281.0000 - val_accuracy: 0.3112\n",
            "Epoch 38/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -10247167.0000 - accuracy: 0.3234 - val_loss: -9508968.0000 - val_accuracy: 0.3112\n",
            "Epoch 39/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -11039582.0000 - accuracy: 0.3234 - val_loss: -10239186.0000 - val_accuracy: 0.3112\n",
            "Epoch 40/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -11885969.0000 - accuracy: 0.3234 - val_loss: -10994930.0000 - val_accuracy: 0.3112\n",
            "Epoch 41/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -12671952.0000 - accuracy: 0.3234 - val_loss: -11786765.0000 - val_accuracy: 0.3112\n",
            "Epoch 42/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -13559723.0000 - accuracy: 0.3234 - val_loss: -12613777.0000 - val_accuracy: 0.3112\n",
            "Epoch 43/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -14584297.0000 - accuracy: 0.3234 - val_loss: -13477794.0000 - val_accuracy: 0.3112\n",
            "Epoch 44/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -15680453.0000 - accuracy: 0.3234 - val_loss: -14389280.0000 - val_accuracy: 0.3112\n",
            "Epoch 45/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -16709159.0000 - accuracy: 0.3234 - val_loss: -15338612.0000 - val_accuracy: 0.3112\n",
            "Epoch 46/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -17789930.0000 - accuracy: 0.3234 - val_loss: -16327686.0000 - val_accuracy: 0.3112\n",
            "Epoch 47/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -18764640.0000 - accuracy: 0.3234 - val_loss: -17353414.0000 - val_accuracy: 0.3112\n",
            "Epoch 48/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -19817764.0000 - accuracy: 0.3234 - val_loss: -18416970.0000 - val_accuracy: 0.3112\n",
            "Epoch 49/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -21330630.0000 - accuracy: 0.3234 - val_loss: -19525884.0000 - val_accuracy: 0.3112\n",
            "Epoch 50/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -22404408.0000 - accuracy: 0.3234 - val_loss: -20685348.0000 - val_accuracy: 0.3112\n",
            "Epoch 51/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -23808840.0000 - accuracy: 0.3234 - val_loss: -21891988.0000 - val_accuracy: 0.3112\n",
            "Epoch 52/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -25187058.0000 - accuracy: 0.3234 - val_loss: -23150216.0000 - val_accuracy: 0.3112\n",
            "Epoch 53/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -26641422.0000 - accuracy: 0.3234 - val_loss: -24438706.0000 - val_accuracy: 0.3112\n",
            "Epoch 54/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -28255496.0000 - accuracy: 0.3234 - val_loss: -25779426.0000 - val_accuracy: 0.3112\n",
            "Epoch 55/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -29820484.0000 - accuracy: 0.3234 - val_loss: -27209582.0000 - val_accuracy: 0.3112\n",
            "Epoch 56/100\n",
            "2230/2230 [==============================] - 8s 3ms/step - loss: -30956934.0000 - accuracy: 0.3234 - val_loss: -28624524.0000 - val_accuracy: 0.3112\n",
            "Epoch 57/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -32682052.0000 - accuracy: 0.3234 - val_loss: -30107758.0000 - val_accuracy: 0.3112\n",
            "Epoch 58/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -34535484.0000 - accuracy: 0.3234 - val_loss: -31650226.0000 - val_accuracy: 0.3112\n",
            "Epoch 59/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -36112216.0000 - accuracy: 0.3234 - val_loss: -33223880.0000 - val_accuracy: 0.3112\n",
            "Epoch 60/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -38078744.0000 - accuracy: 0.3234 - val_loss: -34847676.0000 - val_accuracy: 0.3112\n",
            "Epoch 61/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -40248704.0000 - accuracy: 0.3234 - val_loss: -36521336.0000 - val_accuracy: 0.3112\n",
            "Epoch 62/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -42087280.0000 - accuracy: 0.3234 - val_loss: -38261340.0000 - val_accuracy: 0.3112\n",
            "Epoch 63/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -44007864.0000 - accuracy: 0.3234 - val_loss: -40081472.0000 - val_accuracy: 0.3112\n",
            "Epoch 64/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -45827700.0000 - accuracy: 0.3234 - val_loss: -41939872.0000 - val_accuracy: 0.3112\n",
            "Epoch 65/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -47888480.0000 - accuracy: 0.3234 - val_loss: -43844280.0000 - val_accuracy: 0.3112\n",
            "Epoch 66/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -50418048.0000 - accuracy: 0.3234 - val_loss: -45787444.0000 - val_accuracy: 0.3112\n",
            "Epoch 67/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -51932048.0000 - accuracy: 0.3234 - val_loss: -47799152.0000 - val_accuracy: 0.3112\n",
            "Epoch 68/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -54068480.0000 - accuracy: 0.3234 - val_loss: -49862952.0000 - val_accuracy: 0.3112\n",
            "Epoch 69/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -56698072.0000 - accuracy: 0.3234 - val_loss: -51979332.0000 - val_accuracy: 0.3112\n",
            "Epoch 70/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -59463280.0000 - accuracy: 0.3234 - val_loss: -54184500.0000 - val_accuracy: 0.3112\n",
            "Epoch 71/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -62016620.0000 - accuracy: 0.3234 - val_loss: -56438120.0000 - val_accuracy: 0.3112\n",
            "Epoch 72/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -64162052.0000 - accuracy: 0.3234 - val_loss: -58733036.0000 - val_accuracy: 0.3112\n",
            "Epoch 73/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -67099364.0000 - accuracy: 0.3234 - val_loss: -61106100.0000 - val_accuracy: 0.3112\n",
            "Epoch 74/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -69667256.0000 - accuracy: 0.3234 - val_loss: -63541096.0000 - val_accuracy: 0.3112\n",
            "Epoch 75/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -72610472.0000 - accuracy: 0.3234 - val_loss: -66047488.0000 - val_accuracy: 0.3112\n",
            "Epoch 76/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -75074888.0000 - accuracy: 0.3234 - val_loss: -68612768.0000 - val_accuracy: 0.3112\n",
            "Epoch 77/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -78203416.0000 - accuracy: 0.3234 - val_loss: -71239864.0000 - val_accuracy: 0.3112\n",
            "Epoch 78/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -81418368.0000 - accuracy: 0.3234 - val_loss: -73931528.0000 - val_accuracy: 0.3112\n",
            "Epoch 79/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -84580240.0000 - accuracy: 0.3234 - val_loss: -76696264.0000 - val_accuracy: 0.3112\n",
            "Epoch 80/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -86951336.0000 - accuracy: 0.3234 - val_loss: -79491360.0000 - val_accuracy: 0.3112\n",
            "Epoch 81/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -90390736.0000 - accuracy: 0.3234 - val_loss: -82373048.0000 - val_accuracy: 0.3112\n",
            "Epoch 82/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -93284208.0000 - accuracy: 0.3234 - val_loss: -85356520.0000 - val_accuracy: 0.3112\n",
            "Epoch 83/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -97648920.0000 - accuracy: 0.3234 - val_loss: -88457384.0000 - val_accuracy: 0.3112\n",
            "Epoch 84/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -101261072.0000 - accuracy: 0.3234 - val_loss: -91614648.0000 - val_accuracy: 0.3112\n",
            "Epoch 85/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -104610704.0000 - accuracy: 0.3234 - val_loss: -94827360.0000 - val_accuracy: 0.3112\n",
            "Epoch 86/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -107830408.0000 - accuracy: 0.3234 - val_loss: -98092112.0000 - val_accuracy: 0.3112\n",
            "Epoch 87/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -111905208.0000 - accuracy: 0.3234 - val_loss: -101457264.0000 - val_accuracy: 0.3112\n",
            "Epoch 88/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -115689440.0000 - accuracy: 0.3234 - val_loss: -104890616.0000 - val_accuracy: 0.3112\n",
            "Epoch 89/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -119132768.0000 - accuracy: 0.3234 - val_loss: -108339208.0000 - val_accuracy: 0.3112\n",
            "Epoch 90/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -123778632.0000 - accuracy: 0.3234 - val_loss: -111905672.0000 - val_accuracy: 0.3112\n",
            "Epoch 91/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -128243560.0000 - accuracy: 0.3234 - val_loss: -115592048.0000 - val_accuracy: 0.3112\n",
            "Epoch 92/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -131659912.0000 - accuracy: 0.3234 - val_loss: -119308088.0000 - val_accuracy: 0.3112\n",
            "Epoch 93/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -135555152.0000 - accuracy: 0.3234 - val_loss: -123105032.0000 - val_accuracy: 0.3112\n",
            "Epoch 94/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -140067536.0000 - accuracy: 0.3234 - val_loss: -126977032.0000 - val_accuracy: 0.3112\n",
            "Epoch 95/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -143671248.0000 - accuracy: 0.3234 - val_loss: -130913448.0000 - val_accuracy: 0.3112\n",
            "Epoch 96/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -147531344.0000 - accuracy: 0.3234 - val_loss: -134927744.0000 - val_accuracy: 0.3112\n",
            "Epoch 97/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -151995104.0000 - accuracy: 0.3234 - val_loss: -138999904.0000 - val_accuracy: 0.3112\n",
            "Epoch 98/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -158124608.0000 - accuracy: 0.3234 - val_loss: -143236208.0000 - val_accuracy: 0.3112\n",
            "Epoch 99/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -162380368.0000 - accuracy: 0.3234 - val_loss: -147495376.0000 - val_accuracy: 0.3112\n",
            "Epoch 100/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -167116192.0000 - accuracy: 0.3234 - val_loss: -151834240.0000 - val_accuracy: 0.3112\n",
            "44/44 [==============================] - 0s 2ms/step\n",
            "Positive Sentiment Model - Test Accuracy: 0.29053084648493543\n",
            "Epoch 1/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -200.9143 - accuracy: 0.3234 - val_loss: -706.4871 - val_accuracy: 0.3112\n",
            "Epoch 2/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -2658.1492 - accuracy: 0.3234 - val_loss: -4714.9033 - val_accuracy: 0.3112\n",
            "Epoch 3/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -9491.2969 - accuracy: 0.3234 - val_loss: -13014.5830 - val_accuracy: 0.3112\n",
            "Epoch 4/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -21884.3320 - accuracy: 0.3234 - val_loss: -27152.6992 - val_accuracy: 0.3112\n",
            "Epoch 5/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -41154.0234 - accuracy: 0.3234 - val_loss: -47724.5391 - val_accuracy: 0.3112\n",
            "Epoch 6/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -68557.9453 - accuracy: 0.3234 - val_loss: -75990.4062 - val_accuracy: 0.3112\n",
            "Epoch 7/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -104849.4141 - accuracy: 0.3234 - val_loss: -112728.3438 - val_accuracy: 0.3112\n",
            "Epoch 8/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -149973.0156 - accuracy: 0.3234 - val_loss: -158002.7656 - val_accuracy: 0.3112\n",
            "Epoch 9/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -206265.4844 - accuracy: 0.3234 - val_loss: -214143.8281 - val_accuracy: 0.3112\n",
            "Epoch 10/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -274684.7188 - accuracy: 0.3234 - val_loss: -281668.3438 - val_accuracy: 0.3112\n",
            "Epoch 11/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -359297.8750 - accuracy: 0.3234 - val_loss: -361262.4062 - val_accuracy: 0.3112\n",
            "Epoch 12/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -456812.2812 - accuracy: 0.3234 - val_loss: -454056.1250 - val_accuracy: 0.3112\n",
            "Epoch 13/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -567169.4375 - accuracy: 0.3234 - val_loss: -560703.1250 - val_accuracy: 0.3112\n",
            "Epoch 14/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -696943.9375 - accuracy: 0.3234 - val_loss: -683634.0000 - val_accuracy: 0.3112\n",
            "Epoch 15/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -837134.6250 - accuracy: 0.3234 - val_loss: -820210.1250 - val_accuracy: 0.3112\n",
            "Epoch 16/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -998791.8750 - accuracy: 0.3234 - val_loss: -974237.0625 - val_accuracy: 0.3112\n",
            "Epoch 17/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -1188864.7500 - accuracy: 0.3234 - val_loss: -1148784.8750 - val_accuracy: 0.3112\n",
            "Epoch 18/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -1402650.3750 - accuracy: 0.3234 - val_loss: -1343378.2500 - val_accuracy: 0.3112\n",
            "Epoch 19/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -1615596.8750 - accuracy: 0.3234 - val_loss: -1553262.1250 - val_accuracy: 0.3112\n",
            "Epoch 20/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -1856958.7500 - accuracy: 0.3234 - val_loss: -1784422.5000 - val_accuracy: 0.3112\n",
            "Epoch 21/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -2135136.0000 - accuracy: 0.3234 - val_loss: -2035522.6250 - val_accuracy: 0.3112\n",
            "Epoch 22/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -2416709.7500 - accuracy: 0.3234 - val_loss: -2308066.2500 - val_accuracy: 0.3112\n",
            "Epoch 23/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -2751760.7500 - accuracy: 0.3234 - val_loss: -2608168.7500 - val_accuracy: 0.3112\n",
            "Epoch 24/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -3081326.7500 - accuracy: 0.3234 - val_loss: -2928230.5000 - val_accuracy: 0.3112\n",
            "Epoch 25/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -3460421.7500 - accuracy: 0.3234 - val_loss: -3278375.5000 - val_accuracy: 0.3112\n",
            "Epoch 26/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -3850873.2500 - accuracy: 0.3234 - val_loss: -3653429.0000 - val_accuracy: 0.3112\n",
            "Epoch 27/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -4346177.5000 - accuracy: 0.3234 - val_loss: -4055303.0000 - val_accuracy: 0.3112\n",
            "Epoch 28/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -4762406.0000 - accuracy: 0.3234 - val_loss: -4487937.5000 - val_accuracy: 0.3112\n",
            "Epoch 29/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -5294805.5000 - accuracy: 0.3234 - val_loss: -4949196.0000 - val_accuracy: 0.3112\n",
            "Epoch 30/100\n",
            "2230/2230 [==============================] - 8s 3ms/step - loss: -5820474.5000 - accuracy: 0.3234 - val_loss: -5435411.5000 - val_accuracy: 0.3112\n",
            "Epoch 31/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -6342196.5000 - accuracy: 0.3234 - val_loss: -5953374.5000 - val_accuracy: 0.3112\n",
            "Epoch 32/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -6950915.0000 - accuracy: 0.3234 - val_loss: -6506553.5000 - val_accuracy: 0.3112\n",
            "Epoch 33/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -7670079.0000 - accuracy: 0.3234 - val_loss: -7092076.5000 - val_accuracy: 0.3112\n",
            "Epoch 34/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -8320972.0000 - accuracy: 0.3234 - val_loss: -7707214.5000 - val_accuracy: 0.3112\n",
            "Epoch 35/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -8909113.0000 - accuracy: 0.3234 - val_loss: -8351626.5000 - val_accuracy: 0.3112\n",
            "Epoch 36/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -9684360.0000 - accuracy: 0.3234 - val_loss: -9036112.0000 - val_accuracy: 0.3112\n",
            "Epoch 37/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -10590377.0000 - accuracy: 0.3234 - val_loss: -9765129.0000 - val_accuracy: 0.3112\n",
            "Epoch 38/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -11338117.0000 - accuracy: 0.3234 - val_loss: -10527259.0000 - val_accuracy: 0.3112\n",
            "Epoch 39/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -12290003.0000 - accuracy: 0.3234 - val_loss: -11332535.0000 - val_accuracy: 0.3112\n",
            "Epoch 40/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -13145060.0000 - accuracy: 0.3234 - val_loss: -12170234.0000 - val_accuracy: 0.3112\n",
            "Epoch 41/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -14024358.0000 - accuracy: 0.3234 - val_loss: -13045608.0000 - val_accuracy: 0.3112\n",
            "Epoch 42/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -15112948.0000 - accuracy: 0.3234 - val_loss: -13967563.0000 - val_accuracy: 0.3112\n",
            "Epoch 43/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -16060301.0000 - accuracy: 0.3234 - val_loss: -14911251.0000 - val_accuracy: 0.3112\n",
            "Epoch 44/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -17142032.0000 - accuracy: 0.3234 - val_loss: -15893613.0000 - val_accuracy: 0.3112\n",
            "Epoch 45/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: -18395382.0000 - accuracy: 0.3234 - val_loss: -16948842.0000 - val_accuracy: 0.3112\n",
            "Epoch 46/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -19433884.0000 - accuracy: 0.3234 - val_loss: -18026330.0000 - val_accuracy: 0.3112\n",
            "Epoch 47/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -20621894.0000 - accuracy: 0.3234 - val_loss: -19158942.0000 - val_accuracy: 0.3112\n",
            "Epoch 48/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -22044614.0000 - accuracy: 0.3234 - val_loss: -20335118.0000 - val_accuracy: 0.3112\n",
            "Epoch 49/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -23432550.0000 - accuracy: 0.3234 - val_loss: -21566280.0000 - val_accuracy: 0.3112\n",
            "Epoch 50/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -24770794.0000 - accuracy: 0.3234 - val_loss: -22834400.0000 - val_accuracy: 0.3112\n",
            "Epoch 51/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -26269336.0000 - accuracy: 0.3234 - val_loss: -24162610.0000 - val_accuracy: 0.3112\n",
            "Epoch 52/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -28027456.0000 - accuracy: 0.3234 - val_loss: -25552652.0000 - val_accuracy: 0.3112\n",
            "Epoch 53/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -29297270.0000 - accuracy: 0.3234 - val_loss: -26948728.0000 - val_accuracy: 0.3112\n",
            "Epoch 54/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -31049238.0000 - accuracy: 0.3234 - val_loss: -28423210.0000 - val_accuracy: 0.3112\n",
            "Epoch 55/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -32794140.0000 - accuracy: 0.3234 - val_loss: -29950224.0000 - val_accuracy: 0.3112\n",
            "Epoch 56/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -34468976.0000 - accuracy: 0.3234 - val_loss: -31527062.0000 - val_accuracy: 0.3112\n",
            "Epoch 57/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -36119892.0000 - accuracy: 0.3234 - val_loss: -33152472.0000 - val_accuracy: 0.3112\n",
            "Epoch 58/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -38060640.0000 - accuracy: 0.3234 - val_loss: -34854284.0000 - val_accuracy: 0.3112\n",
            "Epoch 59/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -40068764.0000 - accuracy: 0.3234 - val_loss: -36605568.0000 - val_accuracy: 0.3112\n",
            "Epoch 60/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -42034924.0000 - accuracy: 0.3234 - val_loss: -38397692.0000 - val_accuracy: 0.3112\n",
            "Epoch 61/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -43731308.0000 - accuracy: 0.3234 - val_loss: -40252632.0000 - val_accuracy: 0.3112\n",
            "Epoch 62/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -46139304.0000 - accuracy: 0.3234 - val_loss: -42175056.0000 - val_accuracy: 0.3112\n",
            "Epoch 63/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -48440236.0000 - accuracy: 0.3234 - val_loss: -44181404.0000 - val_accuracy: 0.3112\n",
            "Epoch 64/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -50687108.0000 - accuracy: 0.3234 - val_loss: -46240656.0000 - val_accuracy: 0.3112\n",
            "Epoch 65/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -52917364.0000 - accuracy: 0.3234 - val_loss: -48327084.0000 - val_accuracy: 0.3112\n",
            "Epoch 66/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -55249320.0000 - accuracy: 0.3234 - val_loss: -50501112.0000 - val_accuracy: 0.3112\n",
            "Epoch 67/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -57555044.0000 - accuracy: 0.3234 - val_loss: -52710592.0000 - val_accuracy: 0.3112\n",
            "Epoch 68/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -60221564.0000 - accuracy: 0.3234 - val_loss: -54980096.0000 - val_accuracy: 0.3112\n",
            "Epoch 69/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -62654136.0000 - accuracy: 0.3234 - val_loss: -57278824.0000 - val_accuracy: 0.3112\n",
            "Epoch 70/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -65192916.0000 - accuracy: 0.3234 - val_loss: -59706256.0000 - val_accuracy: 0.3112\n",
            "Epoch 71/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -68778992.0000 - accuracy: 0.3234 - val_loss: -62225272.0000 - val_accuracy: 0.3112\n",
            "Epoch 72/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -71096752.0000 - accuracy: 0.3234 - val_loss: -64797316.0000 - val_accuracy: 0.3112\n",
            "Epoch 73/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -74043808.0000 - accuracy: 0.3234 - val_loss: -67440032.0000 - val_accuracy: 0.3112\n",
            "Epoch 74/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -77039224.0000 - accuracy: 0.3234 - val_loss: -70120712.0000 - val_accuracy: 0.3112\n",
            "Epoch 75/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -79498528.0000 - accuracy: 0.3234 - val_loss: -72888808.0000 - val_accuracy: 0.3112\n",
            "Epoch 76/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -82628336.0000 - accuracy: 0.3234 - val_loss: -75689984.0000 - val_accuracy: 0.3112\n",
            "Epoch 77/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -86289128.0000 - accuracy: 0.3234 - val_loss: -78601640.0000 - val_accuracy: 0.3112\n",
            "Epoch 78/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -89674936.0000 - accuracy: 0.3234 - val_loss: -81614792.0000 - val_accuracy: 0.3112\n",
            "Epoch 79/100\n",
            "2230/2230 [==============================] - 8s 3ms/step - loss: -92688176.0000 - accuracy: 0.3234 - val_loss: -84644784.0000 - val_accuracy: 0.3112\n",
            "Epoch 80/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -96362128.0000 - accuracy: 0.3234 - val_loss: -87758808.0000 - val_accuracy: 0.3112\n",
            "Epoch 81/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -99373800.0000 - accuracy: 0.3234 - val_loss: -90935400.0000 - val_accuracy: 0.3112\n",
            "Epoch 82/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -103941736.0000 - accuracy: 0.3234 - val_loss: -94215904.0000 - val_accuracy: 0.3112\n",
            "Epoch 83/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -106456576.0000 - accuracy: 0.3234 - val_loss: -97541472.0000 - val_accuracy: 0.3112\n",
            "Epoch 84/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -110966272.0000 - accuracy: 0.3234 - val_loss: -101011096.0000 - val_accuracy: 0.3112\n",
            "Epoch 85/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -115249000.0000 - accuracy: 0.3234 - val_loss: -104564136.0000 - val_accuracy: 0.3112\n",
            "Epoch 86/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -118884840.0000 - accuracy: 0.3234 - val_loss: -108148968.0000 - val_accuracy: 0.3112\n",
            "Epoch 87/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -123218232.0000 - accuracy: 0.3234 - val_loss: -111862608.0000 - val_accuracy: 0.3112\n",
            "Epoch 88/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -127816720.0000 - accuracy: 0.3234 - val_loss: -115668192.0000 - val_accuracy: 0.3112\n",
            "Epoch 89/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -131583720.0000 - accuracy: 0.3234 - val_loss: -119530736.0000 - val_accuracy: 0.3112\n",
            "Epoch 90/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -135863984.0000 - accuracy: 0.3234 - val_loss: -123434296.0000 - val_accuracy: 0.3112\n",
            "Epoch 91/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -140254416.0000 - accuracy: 0.3234 - val_loss: -127467240.0000 - val_accuracy: 0.3112\n",
            "Epoch 92/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -144501280.0000 - accuracy: 0.3234 - val_loss: -131605248.0000 - val_accuracy: 0.3112\n",
            "Epoch 93/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -149776912.0000 - accuracy: 0.3234 - val_loss: -135750400.0000 - val_accuracy: 0.3112\n",
            "Epoch 94/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -154134944.0000 - accuracy: 0.3234 - val_loss: -139996032.0000 - val_accuracy: 0.3112\n",
            "Epoch 95/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -159524528.0000 - accuracy: 0.3234 - val_loss: -144379312.0000 - val_accuracy: 0.3112\n",
            "Epoch 96/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -163404944.0000 - accuracy: 0.3234 - val_loss: -148842032.0000 - val_accuracy: 0.3112\n",
            "Epoch 97/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: -168352432.0000 - accuracy: 0.3234 - val_loss: -153334864.0000 - val_accuracy: 0.3112\n",
            "Epoch 98/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: -174543920.0000 - accuracy: 0.3234 - val_loss: -157972032.0000 - val_accuracy: 0.3112\n",
            "Epoch 99/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -178382944.0000 - accuracy: 0.3234 - val_loss: -162643648.0000 - val_accuracy: 0.3112\n",
            "Epoch 100/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: -184751984.0000 - accuracy: 0.3234 - val_loss: -167450448.0000 - val_accuracy: 0.3112\n",
            "44/44 [==============================] - 0s 2ms/step\n",
            "Negative Sentiment Model - Test Accuracy: 0.29053084648493543\n",
            "Epoch 1/100\n",
            "2230/2230 [==============================] - 6s 2ms/step - loss: 0.6356 - accuracy: 0.6753 - val_loss: 0.6263 - val_accuracy: 0.6888\n",
            "Epoch 2/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6322 - accuracy: 0.6766 - val_loss: 0.6243 - val_accuracy: 0.6888\n",
            "Epoch 3/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6310 - accuracy: 0.6766 - val_loss: 0.6231 - val_accuracy: 0.6888\n",
            "Epoch 4/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6317 - accuracy: 0.6766 - val_loss: 0.6221 - val_accuracy: 0.6888\n",
            "Epoch 5/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6311 - accuracy: 0.6766 - val_loss: 0.6220 - val_accuracy: 0.6888\n",
            "Epoch 6/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6313 - accuracy: 0.6766 - val_loss: 0.6208 - val_accuracy: 0.6888\n",
            "Epoch 7/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6294 - accuracy: 0.6766 - val_loss: 0.6212 - val_accuracy: 0.6888\n",
            "Epoch 8/100\n",
            "2230/2230 [==============================] - 8s 3ms/step - loss: 0.6305 - accuracy: 0.6766 - val_loss: 0.6230 - val_accuracy: 0.6888\n",
            "Epoch 9/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.6316 - accuracy: 0.6766 - val_loss: 0.6208 - val_accuracy: 0.6888\n",
            "Epoch 10/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6312 - accuracy: 0.6766 - val_loss: 0.6216 - val_accuracy: 0.6888\n",
            "Epoch 11/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6302 - accuracy: 0.6766 - val_loss: 0.6217 - val_accuracy: 0.6888\n",
            "Epoch 12/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6299 - accuracy: 0.6766 - val_loss: 0.6217 - val_accuracy: 0.6888\n",
            "Epoch 13/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6302 - accuracy: 0.6766 - val_loss: 0.6204 - val_accuracy: 0.6888\n",
            "Epoch 14/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6295 - accuracy: 0.6766 - val_loss: 0.6217 - val_accuracy: 0.6888\n",
            "Epoch 15/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6299 - accuracy: 0.6766 - val_loss: 0.6212 - val_accuracy: 0.6888\n",
            "Epoch 16/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6298 - accuracy: 0.6766 - val_loss: 0.6212 - val_accuracy: 0.6888\n",
            "Epoch 17/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6302 - accuracy: 0.6766 - val_loss: 0.6204 - val_accuracy: 0.6888\n",
            "Epoch 18/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6296 - accuracy: 0.6766 - val_loss: 0.6202 - val_accuracy: 0.6888\n",
            "Epoch 19/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6297 - accuracy: 0.6766 - val_loss: 0.6208 - val_accuracy: 0.6888\n",
            "Epoch 20/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6298 - accuracy: 0.6766 - val_loss: 0.6201 - val_accuracy: 0.6888\n",
            "Epoch 21/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6299 - accuracy: 0.6766 - val_loss: 0.6205 - val_accuracy: 0.6888\n",
            "Epoch 22/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: 0.6300 - accuracy: 0.6766 - val_loss: 0.6206 - val_accuracy: 0.6888\n",
            "Epoch 23/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6298 - accuracy: 0.6766 - val_loss: 0.6210 - val_accuracy: 0.6888\n",
            "Epoch 24/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6202 - val_accuracy: 0.6888\n",
            "Epoch 25/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: 0.6285 - accuracy: 0.6766 - val_loss: 0.6203 - val_accuracy: 0.6888\n",
            "Epoch 26/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6300 - accuracy: 0.6766 - val_loss: 0.6202 - val_accuracy: 0.6888\n",
            "Epoch 27/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6293 - accuracy: 0.6766 - val_loss: 0.6208 - val_accuracy: 0.6888\n",
            "Epoch 28/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6292 - accuracy: 0.6766 - val_loss: 0.6205 - val_accuracy: 0.6888\n",
            "Epoch 29/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6292 - accuracy: 0.6766 - val_loss: 0.6202 - val_accuracy: 0.6888\n",
            "Epoch 30/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6295 - accuracy: 0.6766 - val_loss: 0.6211 - val_accuracy: 0.6888\n",
            "Epoch 31/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6292 - accuracy: 0.6766 - val_loss: 0.6211 - val_accuracy: 0.6888\n",
            "Epoch 32/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6298 - accuracy: 0.6766 - val_loss: 0.6216 - val_accuracy: 0.6888\n",
            "Epoch 33/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6289 - accuracy: 0.6766 - val_loss: 0.6210 - val_accuracy: 0.6888\n",
            "Epoch 34/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: 0.6295 - accuracy: 0.6766 - val_loss: 0.6211 - val_accuracy: 0.6888\n",
            "Epoch 35/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6210 - val_accuracy: 0.6888\n",
            "Epoch 36/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6291 - accuracy: 0.6766 - val_loss: 0.6209 - val_accuracy: 0.6888\n",
            "Epoch 37/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: 0.6296 - accuracy: 0.6766 - val_loss: 0.6205 - val_accuracy: 0.6888\n",
            "Epoch 38/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6289 - accuracy: 0.6766 - val_loss: 0.6238 - val_accuracy: 0.6888\n",
            "Epoch 39/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6293 - accuracy: 0.6766 - val_loss: 0.6219 - val_accuracy: 0.6888\n",
            "Epoch 40/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6287 - accuracy: 0.6766 - val_loss: 0.6229 - val_accuracy: 0.6888\n",
            "Epoch 41/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6292 - accuracy: 0.6766 - val_loss: 0.6227 - val_accuracy: 0.6888\n",
            "Epoch 42/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6293 - accuracy: 0.6766 - val_loss: 0.6217 - val_accuracy: 0.6888\n",
            "Epoch 43/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6288 - accuracy: 0.6766 - val_loss: 0.6204 - val_accuracy: 0.6888\n",
            "Epoch 44/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6295 - accuracy: 0.6766 - val_loss: 0.6215 - val_accuracy: 0.6888\n",
            "Epoch 45/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6295 - accuracy: 0.6766 - val_loss: 0.6212 - val_accuracy: 0.6888\n",
            "Epoch 46/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: 0.6295 - accuracy: 0.6766 - val_loss: 0.6207 - val_accuracy: 0.6888\n",
            "Epoch 47/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6286 - accuracy: 0.6766 - val_loss: 0.6237 - val_accuracy: 0.6888\n",
            "Epoch 48/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6215 - val_accuracy: 0.6888\n",
            "Epoch 49/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6292 - accuracy: 0.6766 - val_loss: 0.6231 - val_accuracy: 0.6888\n",
            "Epoch 50/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6289 - accuracy: 0.6766 - val_loss: 0.6210 - val_accuracy: 0.6888\n",
            "Epoch 51/100\n",
            "2230/2230 [==============================] - 8s 3ms/step - loss: 0.6292 - accuracy: 0.6766 - val_loss: 0.6212 - val_accuracy: 0.6888\n",
            "Epoch 52/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6288 - accuracy: 0.6766 - val_loss: 0.6257 - val_accuracy: 0.6888\n",
            "Epoch 53/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6287 - accuracy: 0.6766 - val_loss: 0.6233 - val_accuracy: 0.6888\n",
            "Epoch 54/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6293 - accuracy: 0.6766 - val_loss: 0.6210 - val_accuracy: 0.6888\n",
            "Epoch 55/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6289 - accuracy: 0.6766 - val_loss: 0.6206 - val_accuracy: 0.6888\n",
            "Epoch 56/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6203 - val_accuracy: 0.6888\n",
            "Epoch 57/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6291 - accuracy: 0.6766 - val_loss: 0.6210 - val_accuracy: 0.6888\n",
            "Epoch 58/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6296 - accuracy: 0.6766 - val_loss: 0.6213 - val_accuracy: 0.6888\n",
            "Epoch 59/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6210 - val_accuracy: 0.6888\n",
            "Epoch 60/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6288 - accuracy: 0.6766 - val_loss: 0.6213 - val_accuracy: 0.6888\n",
            "Epoch 61/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: 0.6288 - accuracy: 0.6766 - val_loss: 0.6210 - val_accuracy: 0.6888\n",
            "Epoch 62/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6291 - accuracy: 0.6766 - val_loss: 0.6228 - val_accuracy: 0.6888\n",
            "Epoch 63/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6291 - accuracy: 0.6766 - val_loss: 0.6231 - val_accuracy: 0.6888\n",
            "Epoch 64/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6214 - val_accuracy: 0.6888\n",
            "Epoch 65/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6288 - accuracy: 0.6766 - val_loss: 0.6227 - val_accuracy: 0.6888\n",
            "Epoch 66/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6289 - accuracy: 0.6766 - val_loss: 0.6224 - val_accuracy: 0.6888\n",
            "Epoch 67/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: 0.6292 - accuracy: 0.6766 - val_loss: 0.6213 - val_accuracy: 0.6888\n",
            "Epoch 68/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6211 - val_accuracy: 0.6888\n",
            "Epoch 69/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6286 - accuracy: 0.6766 - val_loss: 0.6249 - val_accuracy: 0.6888\n",
            "Epoch 70/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6287 - accuracy: 0.6766 - val_loss: 0.6217 - val_accuracy: 0.6888\n",
            "Epoch 71/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6222 - val_accuracy: 0.6888\n",
            "Epoch 72/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6292 - accuracy: 0.6766 - val_loss: 0.6285 - val_accuracy: 0.6888\n",
            "Epoch 73/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6289 - accuracy: 0.6766 - val_loss: 0.6227 - val_accuracy: 0.6888\n",
            "Epoch 74/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6289 - accuracy: 0.6766 - val_loss: 0.6224 - val_accuracy: 0.6888\n",
            "Epoch 75/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6289 - accuracy: 0.6766 - val_loss: 0.6242 - val_accuracy: 0.6888\n",
            "Epoch 76/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6295 - accuracy: 0.6766 - val_loss: 0.6223 - val_accuracy: 0.6888\n",
            "Epoch 77/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6288 - accuracy: 0.6766 - val_loss: 0.6249 - val_accuracy: 0.6888\n",
            "Epoch 78/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6288 - accuracy: 0.6766 - val_loss: 0.6298 - val_accuracy: 0.6888\n",
            "Epoch 79/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6252 - val_accuracy: 0.6888\n",
            "Epoch 80/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6288 - accuracy: 0.6766 - val_loss: 0.6242 - val_accuracy: 0.6888\n",
            "Epoch 81/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6293 - accuracy: 0.6766 - val_loss: 0.6241 - val_accuracy: 0.6888\n",
            "Epoch 82/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: 0.6291 - accuracy: 0.6766 - val_loss: 0.6225 - val_accuracy: 0.6888\n",
            "Epoch 83/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6286 - accuracy: 0.6766 - val_loss: 0.6227 - val_accuracy: 0.6888\n",
            "Epoch 84/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6287 - accuracy: 0.6766 - val_loss: 0.6221 - val_accuracy: 0.6888\n",
            "Epoch 85/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: 0.6287 - accuracy: 0.6766 - val_loss: 0.6258 - val_accuracy: 0.6888\n",
            "Epoch 86/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6289 - accuracy: 0.6766 - val_loss: 0.6225 - val_accuracy: 0.6888\n",
            "Epoch 87/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6287 - accuracy: 0.6766 - val_loss: 0.6221 - val_accuracy: 0.6888\n",
            "Epoch 88/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6285 - accuracy: 0.6766 - val_loss: 0.6231 - val_accuracy: 0.6888\n",
            "Epoch 89/100\n",
            "2230/2230 [==============================] - 8s 3ms/step - loss: 0.6288 - accuracy: 0.6766 - val_loss: 0.6276 - val_accuracy: 0.6888\n",
            "Epoch 90/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6289 - accuracy: 0.6766 - val_loss: 0.6286 - val_accuracy: 0.6888\n",
            "Epoch 91/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6288 - accuracy: 0.6766 - val_loss: 0.6307 - val_accuracy: 0.6888\n",
            "Epoch 92/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6233 - val_accuracy: 0.6888\n",
            "Epoch 93/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6252 - val_accuracy: 0.6888\n",
            "Epoch 94/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6277 - accuracy: 0.6766 - val_loss: 0.6337 - val_accuracy: 0.6888\n",
            "Epoch 95/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6291 - accuracy: 0.6766 - val_loss: 0.6238 - val_accuracy: 0.6888\n",
            "Epoch 96/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6227 - val_accuracy: 0.6888\n",
            "Epoch 97/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6260 - val_accuracy: 0.6888\n",
            "Epoch 98/100\n",
            "2230/2230 [==============================] - 7s 3ms/step - loss: 0.6283 - accuracy: 0.6766 - val_loss: 0.6232 - val_accuracy: 0.6888\n",
            "Epoch 99/100\n",
            "2230/2230 [==============================] - 6s 3ms/step - loss: 0.6284 - accuracy: 0.6766 - val_loss: 0.6279 - val_accuracy: 0.6888\n",
            "Epoch 100/100\n",
            "2230/2230 [==============================] - 5s 2ms/step - loss: 0.6290 - accuracy: 0.6766 - val_loss: 0.6245 - val_accuracy: 0.6888\n",
            "44/44 [==============================] - 0s 2ms/step\n",
            "Neutral Sentiment Model - Test Accuracy: 0.7094691535150646\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Dense, Dropout\n",
        "from sklearn.metrics import accuracy_score\n",
        "from ast import literal_eval\n",
        "\n",
        "# Load the dataset\n",
        "csv_file_path = '/content/drive/MyDrive/combined_data2_updated.csv'\n",
        "df = pd.read_csv(csv_file_path)\n",
        "\n",
        "# Convert string representations of lists to actual lists\n",
        "df['Features'] = df['Features'].apply(literal_eval)\n",
        "\n",
        "# Extract features\n",
        "X = np.vstack(df['Features'])\n",
        "y_very_positive = df['very_positive'].values\n",
        "y_positive = df['positive'].values\n",
        "y_negative = df['negative'].values\n",
        "y_very_negative = df['very_negative'].values\n",
        "\n",
        "# Create the new target variable y_neutral\n",
        "y_neutral = np.where((y_very_positive == 0) & (y_positive == 0) & (y_negative == 0) & (y_very_negative == 0), 1, 0)\n",
        "\n",
        "# Standardize the input features\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train_very_positive, y_test_very_positive, y_train_positive, y_test_positive, y_train_negative, y_test_negative, y_train_very_negative, y_test_very_negative, y_train_neutral, y_test_neutral = train_test_split(\n",
        "    X, y_very_positive, y_positive, y_negative, y_very_negative, y_neutral, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "# Define a multi-class model\n",
        "def multi_class_model(input_dim, num_classes, dropout_rate=0.1):\n",
        "    inputs = Input(shape=(input_dim,))\n",
        "    x = Dense(64, activation='relu')(inputs)\n",
        "    x = Dropout(dropout_rate)(x)\n",
        "    x = Dense(32, activation='relu')(x)\n",
        "    x = Dropout(dropout_rate)(x)\n",
        "    outputs = Dense(num_classes, activation='softmax')(x)  # Use softmax for multi-class classification\n",
        "    model = Model(inputs=inputs, outputs=outputs)\n",
        "    return model\n",
        "\n",
        "# Build and compile the multi-class model\n",
        "num_classes = 5  # Number of classes: very_positive, positive, negative, very_negative, neutral\n",
        "model_multi_class = multi_class_model(input_dim=X_train.shape[1], num_classes=num_classes)\n",
        "model_multi_class.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "\n",
        "# Train the multi-class model\n",
        "model_multi_class.fit(X_train, y_train_very_positive, epochs=100, batch_size=2, validation_split=0.2)\n",
        "\n",
        "# Evaluate the multi-class model\n",
        "test_pred_multi_class = model_multi_class.predict(X_test)\n",
        "test_accuracy_multi_class = accuracy_score(np.argmax(test_pred_multi_class, axis=1), y_test_very_positive)\n",
        "print(f\"Multi-Class Model - Test Accuracy: {test_accuracy_multi_class}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQuQp7zVXtCQ",
        "outputId": "cc7466d8-f367-4b7b-f981-cdce7eb8e1fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "2230/2230 [==============================] - 13s 5ms/step - loss: 0.4713 - accuracy: 0.8511 - val_loss: 0.4313 - val_accuracy: 0.8457\n",
            "Epoch 2/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4223 - accuracy: 0.8536 - val_loss: 0.4385 - val_accuracy: 0.8457\n",
            "Epoch 3/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4227 - accuracy: 0.8536 - val_loss: 0.4347 - val_accuracy: 0.8457\n",
            "Epoch 4/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4221 - accuracy: 0.8536 - val_loss: 0.4334 - val_accuracy: 0.8457\n",
            "Epoch 5/100\n",
            "2230/2230 [==============================] - 11s 5ms/step - loss: 0.4211 - accuracy: 0.8536 - val_loss: 0.4427 - val_accuracy: 0.8457\n",
            "Epoch 6/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4206 - accuracy: 0.8536 - val_loss: 0.4311 - val_accuracy: 0.8457\n",
            "Epoch 7/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4218 - accuracy: 0.8536 - val_loss: 0.4293 - val_accuracy: 0.8457\n",
            "Epoch 8/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4200 - accuracy: 0.8536 - val_loss: 0.4305 - val_accuracy: 0.8457\n",
            "Epoch 9/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4191 - accuracy: 0.8536 - val_loss: 0.4305 - val_accuracy: 0.8457\n",
            "Epoch 10/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4184 - accuracy: 0.8536 - val_loss: 0.4341 - val_accuracy: 0.8457\n",
            "Epoch 11/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4193 - accuracy: 0.8536 - val_loss: 0.4327 - val_accuracy: 0.8457\n",
            "Epoch 12/100\n",
            "2230/2230 [==============================] - 11s 5ms/step - loss: 0.4179 - accuracy: 0.8536 - val_loss: 0.4323 - val_accuracy: 0.8457\n",
            "Epoch 13/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4192 - accuracy: 0.8536 - val_loss: 0.4341 - val_accuracy: 0.8457\n",
            "Epoch 14/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4187 - accuracy: 0.8536 - val_loss: 0.4303 - val_accuracy: 0.8457\n",
            "Epoch 15/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4172 - accuracy: 0.8536 - val_loss: 0.4301 - val_accuracy: 0.8457\n",
            "Epoch 16/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4190 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 17/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4174 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 18/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4183 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 19/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4172 - accuracy: 0.8536 - val_loss: 0.4301 - val_accuracy: 0.8457\n",
            "Epoch 20/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4171 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 21/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4170 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 22/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4171 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 23/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4161 - accuracy: 0.8536 - val_loss: 0.4309 - val_accuracy: 0.8457\n",
            "Epoch 24/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4169 - accuracy: 0.8536 - val_loss: 0.4303 - val_accuracy: 0.8457\n",
            "Epoch 25/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4166 - accuracy: 0.8536 - val_loss: 0.4310 - val_accuracy: 0.8457\n",
            "Epoch 26/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4170 - accuracy: 0.8536 - val_loss: 0.4312 - val_accuracy: 0.8457\n",
            "Epoch 27/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4172 - accuracy: 0.8536 - val_loss: 0.4305 - val_accuracy: 0.8457\n",
            "Epoch 28/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4171 - accuracy: 0.8536 - val_loss: 0.4299 - val_accuracy: 0.8457\n",
            "Epoch 29/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4167 - accuracy: 0.8536 - val_loss: 0.4312 - val_accuracy: 0.8457\n",
            "Epoch 30/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4169 - accuracy: 0.8536 - val_loss: 0.4299 - val_accuracy: 0.8457\n",
            "Epoch 31/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4173 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 32/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4161 - accuracy: 0.8536 - val_loss: 0.4302 - val_accuracy: 0.8457\n",
            "Epoch 33/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4171 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 34/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4166 - accuracy: 0.8536 - val_loss: 0.4306 - val_accuracy: 0.8457\n",
            "Epoch 35/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4170 - accuracy: 0.8536 - val_loss: 0.4303 - val_accuracy: 0.8457\n",
            "Epoch 36/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4172 - accuracy: 0.8536 - val_loss: 0.4300 - val_accuracy: 0.8457\n",
            "Epoch 37/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4168 - accuracy: 0.8536 - val_loss: 0.4301 - val_accuracy: 0.8457\n",
            "Epoch 38/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4167 - accuracy: 0.8536 - val_loss: 0.4302 - val_accuracy: 0.8457\n",
            "Epoch 39/100\n",
            "2230/2230 [==============================] - 11s 5ms/step - loss: 0.4164 - accuracy: 0.8536 - val_loss: 0.4298 - val_accuracy: 0.8457\n",
            "Epoch 40/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4168 - accuracy: 0.8536 - val_loss: 0.4299 - val_accuracy: 0.8457\n",
            "Epoch 41/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4167 - accuracy: 0.8536 - val_loss: 0.4293 - val_accuracy: 0.8457\n",
            "Epoch 42/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4164 - accuracy: 0.8536 - val_loss: 0.4293 - val_accuracy: 0.8457\n",
            "Epoch 43/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4166 - accuracy: 0.8536 - val_loss: 0.4301 - val_accuracy: 0.8457\n",
            "Epoch 44/100\n",
            "2230/2230 [==============================] - 11s 5ms/step - loss: 0.4166 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 45/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4166 - accuracy: 0.8536 - val_loss: 0.4301 - val_accuracy: 0.8457\n",
            "Epoch 46/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4161 - accuracy: 0.8536 - val_loss: 0.4303 - val_accuracy: 0.8457\n",
            "Epoch 47/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4165 - accuracy: 0.8536 - val_loss: 0.4299 - val_accuracy: 0.8457\n",
            "Epoch 48/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4294 - val_accuracy: 0.8457\n",
            "Epoch 49/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4292 - val_accuracy: 0.8457\n",
            "Epoch 50/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4160 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 51/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4165 - accuracy: 0.8536 - val_loss: 0.4294 - val_accuracy: 0.8457\n",
            "Epoch 52/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4165 - accuracy: 0.8536 - val_loss: 0.4294 - val_accuracy: 0.8457\n",
            "Epoch 53/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4303 - val_accuracy: 0.8457\n",
            "Epoch 54/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4160 - accuracy: 0.8536 - val_loss: 0.4293 - val_accuracy: 0.8457\n",
            "Epoch 55/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4298 - val_accuracy: 0.8457\n",
            "Epoch 56/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4302 - val_accuracy: 0.8457\n",
            "Epoch 57/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4160 - accuracy: 0.8536 - val_loss: 0.4294 - val_accuracy: 0.8457\n",
            "Epoch 58/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 59/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4161 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 60/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 61/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4168 - accuracy: 0.8536 - val_loss: 0.4299 - val_accuracy: 0.8457\n",
            "Epoch 62/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4165 - accuracy: 0.8536 - val_loss: 0.4301 - val_accuracy: 0.8457\n",
            "Epoch 63/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4167 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 64/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4164 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 65/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4317 - val_accuracy: 0.8457\n",
            "Epoch 66/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 67/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4161 - accuracy: 0.8536 - val_loss: 0.4299 - val_accuracy: 0.8457\n",
            "Epoch 68/100\n",
            "2230/2230 [==============================] - 11s 5ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 69/100\n",
            "2230/2230 [==============================] - 12s 6ms/step - loss: 0.4160 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 70/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4295 - val_accuracy: 0.8457\n",
            "Epoch 71/100\n",
            "2230/2230 [==============================] - 11s 5ms/step - loss: 0.4161 - accuracy: 0.8536 - val_loss: 0.4298 - val_accuracy: 0.8457\n",
            "Epoch 72/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4310 - val_accuracy: 0.8457\n",
            "Epoch 73/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4165 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 74/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4305 - val_accuracy: 0.8457\n",
            "Epoch 75/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4161 - accuracy: 0.8536 - val_loss: 0.4298 - val_accuracy: 0.8457\n",
            "Epoch 76/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 77/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4164 - accuracy: 0.8536 - val_loss: 0.4303 - val_accuracy: 0.8457\n",
            "Epoch 78/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4304 - val_accuracy: 0.8457\n",
            "Epoch 79/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4165 - accuracy: 0.8536 - val_loss: 0.4303 - val_accuracy: 0.8457\n",
            "Epoch 80/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 81/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4303 - val_accuracy: 0.8457\n",
            "Epoch 82/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4304 - val_accuracy: 0.8457\n",
            "Epoch 83/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 84/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4161 - accuracy: 0.8536 - val_loss: 0.4304 - val_accuracy: 0.8457\n",
            "Epoch 85/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4166 - accuracy: 0.8536 - val_loss: 0.4298 - val_accuracy: 0.8457\n",
            "Epoch 86/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 87/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 88/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4295 - val_accuracy: 0.8457\n",
            "Epoch 89/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4295 - val_accuracy: 0.8457\n",
            "Epoch 90/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4298 - val_accuracy: 0.8457\n",
            "Epoch 91/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4302 - val_accuracy: 0.8457\n",
            "Epoch 92/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4160 - accuracy: 0.8536 - val_loss: 0.4295 - val_accuracy: 0.8457\n",
            "Epoch 93/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4165 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 94/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4160 - accuracy: 0.8536 - val_loss: 0.4299 - val_accuracy: 0.8457\n",
            "Epoch 95/100\n",
            "2230/2230 [==============================] - 10s 4ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4303 - val_accuracy: 0.8457\n",
            "Epoch 96/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4313 - val_accuracy: 0.8457\n",
            "Epoch 97/100\n",
            "2230/2230 [==============================] - 11s 5ms/step - loss: 0.4163 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "Epoch 98/100\n",
            "2230/2230 [==============================] - 8s 4ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4297 - val_accuracy: 0.8457\n",
            "Epoch 99/100\n",
            "2230/2230 [==============================] - 10s 5ms/step - loss: 0.4162 - accuracy: 0.8536 - val_loss: 0.4295 - val_accuracy: 0.8457\n",
            "Epoch 100/100\n",
            "2230/2230 [==============================] - 9s 4ms/step - loss: 0.4166 - accuracy: 0.8536 - val_loss: 0.4296 - val_accuracy: 0.8457\n",
            "44/44 [==============================] - 0s 3ms/step\n",
            "Multi-Class Model - Test Accuracy: 0.8522238163558106\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Input, concatenate\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Load your dataset\n",
        "# df = pd.read_csv('/path/to/your/meme_dataset.csv')\n",
        "\n",
        "# Preprocess the sentiment labels\n",
        "le = LabelEncoder()\n",
        "df['label'] = le.fit_transform(df['overall_sentiment'])\n",
        "\n",
        "# Fill NaN values in the 'text_corrected' column with an empty string\n",
        "df['text_corrected'].fillna('', inplace=True)\n",
        "\n",
        "# Frequency encode the text features\n",
        "vectorizer = CountVectorizer()\n",
        "text_features = vectorizer.fit_transform(df['text_corrected']).toarray()\n",
        "\n",
        "# Concatenate text features with numerical features\n",
        "combined_features = np.hstack((text_features, np.vstack(df['Features'].apply(eval).values)))\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(combined_features, df['label'].values, test_size=0.2, random_state=42)\n",
        "\n",
        "# Neural Network Model\n",
        "model = Sequential([\n",
        "    Input(shape=(combined_features.shape[1],)),\n",
        "    Dense(512, activation='relu'),\n",
        "    Dense(256, activation='relu'),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(len(le.classes_), activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer=Adam(learning_rate=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
        "\n",
        "# Evaluate the model\n",
        "test_accuracy_nn = model.evaluate(X_test, y_test)[1]\n",
        "print(f\"Neural Network Test Accuracy: {test_accuracy_nn * 100:.2f}%\")\n",
        "\n",
        "# Random Forest Model\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Train the Random Forest model\n",
        "rf_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predictions on the test set\n",
        "y_pred_rf = rf_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the Random Forest model\n",
        "test_accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
        "print(f\"Random Forest Test Accuracy: {test_accuracy_rf * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "muvLlP7vaMug",
        "outputId": "5d490e74-b1db-4f7b-a67f-0f37c2007281"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "140/140 [==============================] - 5s 14ms/step - loss: 1.3174 - accuracy: 0.4337 - val_loss: 1.3268 - val_accuracy: 0.4072\n",
            "Epoch 2/10\n",
            "140/140 [==============================] - 1s 7ms/step - loss: 0.9603 - accuracy: 0.6039 - val_loss: 1.5538 - val_accuracy: 0.3417\n",
            "Epoch 3/10\n",
            "140/140 [==============================] - 1s 7ms/step - loss: 0.3130 - accuracy: 0.8937 - val_loss: 2.5071 - val_accuracy: 0.3462\n",
            "Epoch 4/10\n",
            "140/140 [==============================] - 1s 7ms/step - loss: 0.1004 - accuracy: 0.9751 - val_loss: 2.8469 - val_accuracy: 0.3650\n",
            "Epoch 5/10\n",
            "140/140 [==============================] - 1s 7ms/step - loss: 0.0360 - accuracy: 0.9895 - val_loss: 3.3636 - val_accuracy: 0.3570\n",
            "Epoch 6/10\n",
            "140/140 [==============================] - 1s 8ms/step - loss: 0.0297 - accuracy: 0.9913 - val_loss: 3.3317 - val_accuracy: 0.3354\n",
            "Epoch 7/10\n",
            "140/140 [==============================] - 1s 7ms/step - loss: 0.0181 - accuracy: 0.9924 - val_loss: 3.6252 - val_accuracy: 0.3480\n",
            "Epoch 8/10\n",
            "140/140 [==============================] - 1s 7ms/step - loss: 0.0159 - accuracy: 0.9937 - val_loss: 3.6977 - val_accuracy: 0.3534\n",
            "Epoch 9/10\n",
            "140/140 [==============================] - 1s 8ms/step - loss: 0.0109 - accuracy: 0.9948 - val_loss: 4.0516 - val_accuracy: 0.3202\n",
            "Epoch 10/10\n",
            "140/140 [==============================] - 1s 6ms/step - loss: 0.0109 - accuracy: 0.9946 - val_loss: 3.9515 - val_accuracy: 0.3336\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 3.5118 - accuracy: 0.3615\n",
            "Neural Network Test Accuracy: 36.15%\n",
            "Random Forest Test Accuracy: 45.12%\n"
          ]
        }
      ]
    }
  ]
}