Text_emotion_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5818a677",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a64069d8",
   "metadata": {
    "id": "a64069d8"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7312/1726719974.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorpus\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstopwords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mporter\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mPorterStemmer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# Load Text Cleaning Pkgs\n",
    "#import neattext.functions as nfx\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "import tensorflow as tf \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae4b83e0",
   "metadata": {
    "id": "ae4b83e0",
    "outputId": "f03dd728-2318-4986-f37a-ec6db1aa69b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting neattext\n",
      "  Downloading neattext-0.1.3-py3-none-any.whl (114 kB)\n",
      "Installing collected packages: neattext\n",
      "Successfully installed neattext-0.1.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#pip install neattext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2f6d4506",
   "metadata": {
    "id": "2f6d4506"
   },
   "outputs": [],
   "source": [
    "# train=pd.read_table('train.txt', delimiter = ';', header=None, )\n",
    "# val=pd.read_table('val.txt', delimiter = ';', header=None, )\n",
    "# test=pd.read_table('test.txt', delimiter = ';', header=None, )\n",
    "\n",
    "# data = pd.concat([train ,  val , test])\n",
    "# data.columns = [\"text\", \"label\"]\n",
    "# data\n",
    "\n",
    "#data.to_csv(\"emotion_final.csv\")\n",
    "\n",
    "data = pd.read_csv(\"emotion_final.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c85a7c21",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c85a7c21",
    "outputId": "7b632bb2-f81f-4c1e-fa6a-7b472b182d91"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 3)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0847cf4f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0847cf4f",
    "outputId": "bce70057-5088-4162-9a91-4f2bdbed53eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0     int64\n",
       "text          object\n",
       "label         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "36b853a8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "36b853a8",
    "outputId": "b1865d5c-fd4b-48fd-bc03-db22877c49db"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "text          0\n",
       "label         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7a563fb0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7a563fb0",
    "outputId": "e8fd02e9-6bf6-4b3a-a061-9f40d005bd9d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "joy         6761\n",
       "sadness     5797\n",
       "anger       2709\n",
       "fear        2373\n",
       "love        1641\n",
       "surprise     719\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6bbab29c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 889
    },
    "id": "6bbab29c",
    "outputId": "abaaa36f-cb57-4a39-d151-a0746bc7d622"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "data['label'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "174af0b8",
   "metadata": {
    "id": "174af0b8"
   },
   "outputs": [],
   "source": [
    "#keyword finding for each emotion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fff831cf",
   "metadata": {
    "id": "fff831cf"
   },
   "outputs": [],
   "source": [
    "#from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4ba74ed",
   "metadata": {
    "id": "e4ba74ed",
    "outputId": "7b626fa0-adeb-4c7b-ca32-8c96980520d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting textblob\n",
      "  Downloading textblob-0.17.1-py2.py3-none-any.whl (636 kB)\n",
      "Requirement already satisfied: nltk>=3.1 in d:\\jupyter\\lib\\site-packages (from textblob) (3.6.5)\n",
      "Requirement already satisfied: click in d:\\jupyter\\lib\\site-packages (from nltk>=3.1->textblob) (8.0.3)\n",
      "Requirement already satisfied: joblib in d:\\jupyter\\lib\\site-packages (from nltk>=3.1->textblob) (1.1.0)\n",
      "Requirement already satisfied: regex>=2021.8.3 in d:\\jupyter\\lib\\site-packages (from nltk>=3.1->textblob) (2021.8.3)\n",
      "Requirement already satisfied: tqdm in d:\\jupyter\\lib\\site-packages (from nltk>=3.1->textblob) (4.62.3)\n",
      "Requirement already satisfied: colorama in d:\\jupyter\\lib\\site-packages (from click->nltk>=3.1->textblob) (0.4.4)\n",
      "Installing collected packages: textblob\n",
      "Successfully installed textblob-0.17.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#pip install textblob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f168506e",
   "metadata": {
    "id": "f168506e"
   },
   "outputs": [],
   "source": [
    "#text preprocessing\n",
    "ps = PorterStemmer()\n",
    "\n",
    "def preprocess(line):\n",
    "    review = re.sub('[^a-zA-Z]', ' ', line) #leave only characters from a to z\n",
    "    review = review.lower() #lower the text\n",
    "    review = review.split() #turn string into list of words\n",
    "    #apply Stemming \n",
    "    review = [ps.stem(word) for word in review if not word in stopwords.words('english')] #delete stop words like I, and ,OR   review = ' '.join(review)\n",
    "    #trun list into sentences\n",
    "    return \" \".join(review)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "jQN99Jch0Q0f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jQN99Jch0Q0f",
    "outputId": "1cffcd42-f58f-436d-bfe7-6753425574c0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5b0fa873",
   "metadata": {
    "id": "5b0fa873"
   },
   "outputs": [],
   "source": [
    "data['text']=data['text'].apply(lambda x: preprocess(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a2c5bdc2",
   "metadata": {
    "id": "a2c5bdc2"
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "data['N_label'] = label_encoder.fit_transform(data['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "13426928",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "13426928",
    "outputId": "0716fc12-b84e-4ecd-9109-4019b0855b11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                        didnt feel humili\n",
       "1        go feel hopeless damn hope around someon care ...\n",
       "2                     im grab minut post feel greedi wrong\n",
       "3           ever feel nostalg fireplac know still properti\n",
       "4                                             feel grouchi\n",
       "                               ...                        \n",
       "19995    keep feel like someon unkind wrong think get b...\n",
       "19996              im feel littl cranki neg doctor appoint\n",
       "19997                feel use peopl give great feel achiev\n",
       "19998    im feel comfort derbi feel though start step s...\n",
       "19999    feel weird meet w peopl text like dont talk fa...\n",
       "Name: text, Length: 20000, dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8309b5fc",
   "metadata": {
    "id": "8309b5fc"
   },
   "outputs": [],
   "source": [
    "# Creating the Bag of Words model by applying Countvectorizer -convert textual data to numerical data\n",
    "\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "cv = CountVectorizer(max_features=5000,ngram_range=(1,3))#example: the course was long-> [the,the course,the course was,course, course was, course was long,...]\n",
    "\n",
    "data_cv = cv.fit_transform(data['text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "10c89e90",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "10c89e90",
    "outputId": "ddead511-85a8-4e2a-db5e-883499636d31"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9db8c81b",
   "metadata": {
    "id": "9db8c81b"
   },
   "outputs": [],
   "source": [
    "#X_train, X_test, y_train, y_test=data_cv,test_cv,train['N_label'],test['N_label']\n",
    "X_train, X_test, y_train, y_test =train_test_split(data_cv, data['N_label'], test_size=0.25, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "24cd90a0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "24cd90a0",
    "outputId": "697ce71f-c421-476c-aed8-8e2111d2fae5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1500/1500 [==============================] - 5s 3ms/step - loss: 1.0069 - accuracy: 0.6515\n",
      "Epoch 2/10\n",
      "1500/1500 [==============================] - 8s 5ms/step - loss: 0.2888 - accuracy: 0.9087\n",
      "Epoch 3/10\n",
      "1500/1500 [==============================] - 5s 3ms/step - loss: 0.1475 - accuracy: 0.9540\n",
      "Epoch 4/10\n",
      "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0922 - accuracy: 0.9707\n",
      "Epoch 5/10\n",
      "1500/1500 [==============================] - 4s 3ms/step - loss: 0.0604 - accuracy: 0.9827\n",
      "Epoch 6/10\n",
      "1500/1500 [==============================] - 4s 3ms/step - loss: 0.0434 - accuracy: 0.9871\n",
      "Epoch 7/10\n",
      "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0336 - accuracy: 0.9909\n",
      "Epoch 8/10\n",
      "1500/1500 [==============================] - 4s 3ms/step - loss: 0.0265 - accuracy: 0.9916\n",
      "Epoch 9/10\n",
      "1500/1500 [==============================] - 4s 3ms/step - loss: 0.0220 - accuracy: 0.9936\n",
      "Epoch 10/10\n",
      "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0185 - accuracy: 0.9948\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0115 - accuracy: 0.9964\n",
      "Accuracy: 99.64\n"
     ]
    }
   ],
   "source": [
    "# first neural network with keras tutorial\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "# load the dataset\n",
    "# split into input (X) and output (y) variables\n",
    "# define the keras model\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_shape=(X_train.shape[1],), activation='relu'))\n",
    "model.add(Dense(8, activation='relu'))\n",
    "model.add(Dense(6, activation='softmax'))\n",
    "# compile the keras model\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "# fit the keras model on the dataset\n",
    "model.fit(X_train, y_train, epochs=10, batch_size=10)\n",
    "# evaluate the keras model\n",
    "_, accuracy = model.evaluate(X_train, y_train)\n",
    "print('Accuracy: %.2f' % (accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c3gdbSlP5D-b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c3gdbSlP5D-b",
    "outputId": "340e53a7-22de-4b6d-c603-d7de5cebee93"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 3ms/step - loss: 0.8039 - accuracy: 0.8502\n",
      "Accuracy: 85.02\n"
     ]
    }
   ],
   "source": [
    "_, accuracy = model.evaluate(X_test, y_test)\n",
    "print('Accuracy: %.2f' % (accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "UxcZeZL55SIm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UxcZeZL55SIm",
    "outputId": "9ed84ff7-b467-4f7d-b9bd-64606713a0cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter sentence for prediction : with everythin,with everybody, with all this!!!\n"
     ]
    }
   ],
   "source": [
    "new_sen = input(\"Enter sentence for prediction : \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "WF4EBgqx5x6r",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "WF4EBgqx5x6r",
    "outputId": "cb82936f-e22b-4f5a-9eb2-3ae8ecfd1b03"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'with everythin,with everybody, with all this!!!'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_sen\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "OFYjzvdS5SXp",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "OFYjzvdS5SXp",
    "outputId": "419ef16a-04f8-43d0-8502-f7a447170316"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 62ms/step\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'love'"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_sen=preprocess(new_sen)\n",
    "array = cv.transform([new_sen]).toarray()\n",
    "pred = model.predict(array)\n",
    "a=np.argmax(pred, axis=1)\n",
    "label_encoder.inverse_transform(a)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "aMY_l3v-5SkW",
   "metadata": {
    "id": "aMY_l3v-5SkW"
   },
   "outputs": [],
   "source": [
    "tf.keras.models.save_model(model,'my_model.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "s-kL058FDX-i",
   "metadata": {
    "id": "s-kL058FDX-i"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(label_encoder, open('encoder.pkl', 'wb'))\n",
    "pickle.dump(cv, open('CountVectorizer.pkl', 'wb'))"
   ]
  }
 ],
 "metadata": {
  "colab": {
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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