Natural_Language_Processing_Sentiment_Analysis (1) (1).ipynb

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      "source": [
        "import pandas as pd"
      ]
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      "source": [
        "data = pd.read_csv('twitter_training.csv')"
      ],
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        "data.columns = ['ID', 'Brand', 'Sentiment', 'Phrase']"
      ],
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              "     ID        Brand Sentiment  \\\n",
              "0  2401  Borderlands  Positive   \n",
              "1  2401  Borderlands  Positive   \n",
              "2  2401  Borderlands  Positive   \n",
              "3  2401  Borderlands  Positive   \n",
              "4  2401  Borderlands  Positive   \n",
              "\n",
              "                                              Phrase  \n",
              "0  I am coming to the borders and I will kill you...  \n",
              "1  im getting on borderlands and i will kill you ...  \n",
              "2  im coming on borderlands and i will murder you...  \n",
              "3  im getting on borderlands 2 and i will murder ...  \n",
              "4  im getting into borderlands and i can murder y...  "
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 74681 entries, 0 to 74680\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   ID         74681 non-null  int64 \n",
            " 1   Brand      74681 non-null  object\n",
            " 2   Sentiment  74681 non-null  object\n",
            " 3   Phrase     73995 non-null  object\n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 2.3+ MB\n"
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        "data.isna().sum()"
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          "data": {
            "text/plain": [
              "ID             0\n",
              "Brand          0\n",
              "Sentiment      0\n",
              "Phrase       686\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
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      "cell_type": "code",
      "source": [
        "data = data.dropna()"
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        "id": "Gjkn0T-fTEFc"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID           0\n",
              "Brand        0\n",
              "Sentiment    0\n",
              "Phrase       0\n",
              "dtype: int64"
            ]
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          "execution_count": 32
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            "text/plain": [
              "     ID        Brand Sentiment  \\\n",
              "0  2401  Borderlands  Positive   \n",
              "1  2401  Borderlands  Positive   \n",
              "2  2401  Borderlands  Positive   \n",
              "3  2401  Borderlands  Positive   \n",
              "4  2401  Borderlands  Positive   \n",
              "\n",
              "                                              Phrase  \n",
              "0  I am coming to the borders and I will kill you...  \n",
              "1  im getting on borderlands and i will kill you ...  \n",
              "2  im coming on borderlands and i will murder you...  \n",
              "3  im getting on borderlands 2 and i will murder ...  \n",
              "4  im getting into borderlands and i can murder y...  "
            ],
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              "      <td>Positive</td>\n",
              "      <td>im coming on borderlands and i will murder you...</td>\n",
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              "      <td>2401</td>\n",
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              "      <td>2401</td>\n",
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              "      <td>Positive</td>\n",
              "      <td>im getting into borderlands and i can murder y...</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "data['Sentiment'].unique()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "YqR6pp2ziLI4",
        "outputId": "82125829-aeed-4d62-d217-c88e0d5ccd19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Positive', 'Neutral', 'Negative', 'Irrelevant'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "data['Sentiment'] = le.fit_transform(data['Sentiment'])"
      ],
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        "id": "WukpECiFTlx6"
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        "le.classes_"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Irrelevant', 'Negative', 'Neutral', 'Positive'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     ID        Brand  Sentiment  \\\n",
              "0  2401  Borderlands          3   \n",
              "1  2401  Borderlands          3   \n",
              "2  2401  Borderlands          3   \n",
              "3  2401  Borderlands          3   \n",
              "4  2401  Borderlands          3   \n",
              "\n",
              "                                              Phrase  \n",
              "0  I am coming to the borders and I will kill you...  \n",
              "1  im getting on borderlands and i will kill you ...  \n",
              "2  im coming on borderlands and i will murder you...  \n",
              "3  im getting on borderlands 2 and i will murder ...  \n",
              "4  im getting into borderlands and i can murder y...  "
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              "\n",
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Brand</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Phrase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>im getting on borderlands and i will kill you ...</td>\n",
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              "      <td>2401</td>\n",
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              "      <td>im coming on borderlands and i will murder you...</td>\n",
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              "      <td>2401</td>\n",
              "      <td>Borderlands</td>\n",
              "      <td>3</td>\n",
              "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2401</td>\n",
              "      <td>Borderlands</td>\n",
              "      <td>3</td>\n",
              "      <td>im getting into borderlands and i can murder y...</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature Generation Using Bag of Words\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from nltk.tokenize import RegexpTokenizer"
      ],
      "metadata": {
        "id": "-PtyzX-iTJ--"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "token = RegexpTokenizer(r'[a-zA-Z0-9]+')\n",
        "cv = CountVectorizer(lowercase=True, stop_words='english',ngram_range = (1,1),tokenizer = token.tokenize)\n",
        "text_counts= cv.fit_transform(data['Phrase'])"
      ],
      "metadata": {
        "id": "bl2Wh3M8TVsb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting in training and testing data\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(text_counts, data['Sentiment'], test_size = 0.3, random_state=29)"
      ],
      "metadata": {
        "id": "aEB54syTatka"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model Building\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier()\n",
        "dt.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KwnZwIV1a4d3",
        "outputId": "b4652804-978a-410a-a07b-910918aa2f7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predictions \n",
        "pred = dt.predict(X_test)"
      ],
      "metadata": {
        "id": "csL27wN0bjo6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNCeVDWCfBPC",
        "outputId": "c9a0ecc8-db2c-413a-f94c-4369e7fafbd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.75      0.79      3875\n",
            "           1       0.85      0.84      0.84      6755\n",
            "           2       0.84      0.81      0.82      5419\n",
            "           3       0.77      0.85      0.81      6150\n",
            "\n",
            "    accuracy                           0.82     22199\n",
            "   macro avg       0.82      0.81      0.82     22199\n",
            "weighted avg       0.82      0.82      0.82     22199\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#tf-idf \n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tf=TfidfVectorizer()\n",
        "text_tf= tf.fit_transform(data['Phrase'])"
      ],
      "metadata": {
        "id": "sZ33nJIQicTf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train1, X_test1, y_train1, y_test1 = train_test_split(text_tf, data['Sentiment'], test_size = 0.3, random_state=29)"
      ],
      "metadata": {
        "id": "-yZouCj5o4XT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt.fit(X_train1, y_train1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40-NdrBro-DU",
        "outputId": "c6726cb7-c68e-406a-ada0-bace3ce2dcc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred1 = dt.predict(X_test1)"
      ],
      "metadata": {
        "id": "3dZIsHLspdK2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test1, pred1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4GFR6dnpuei",
        "outputId": "6e5f15ba-d578-47a7-9232-b6b52d0b6c47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.66      0.68      3875\n",
            "           1       0.79      0.78      0.78      6755\n",
            "           2       0.73      0.76      0.74      5419\n",
            "           3       0.75      0.76      0.76      6150\n",
            "\n",
            "    accuracy                           0.75     22199\n",
            "   macro avg       0.74      0.74      0.74     22199\n",
            "weighted avg       0.75      0.75      0.75     22199\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('news.csv')"
      ],
      "metadata": {
        "id": "b6r0C3sX-gDK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qTbNTaBE-f73",
        "outputId": "e8a0f7bf-fb71-43f6-b641-0a19f5d929a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   publish_date                                           headline\n",
              "0      20030219  aba decides against community broadcasting lic...\n",
              "1      20030219     act fire witnesses must be aware of defamation\n",
              "2      20030219     a g calls for infrastructure protection summit\n",
              "3      20030219           air nz staff in aust strike for pay rise\n",
              "4      20030219      air nz strike to affect australian travellers"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8390cd3-5969-402b-93eb-83edc28bcfa8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>publish_date</th>\n",
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              "      <td>20030219</td>\n",
              "      <td>a g calls for infrastructure protection summit</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>20030219</td>\n",
              "      <td>air nz staff in aust strike for pay rise</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20030219</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8390cd3-5969-402b-93eb-83edc28bcfa8')\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b8390cd3-5969-402b-93eb-83edc28bcfa8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b8390cd3-5969-402b-93eb-83edc28bcfa8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('vader_lexicon')"
      ],
      "metadata": {
        "id": "DbCIfOk--joK",
        "outputId": "9700f3a9-95a3-4328-ce2b-936a317ec907",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SvEzoxSw-nLJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}