main.ipynb

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      "source": [
        "## **Stock News Headlines Sentimental Analysis using NLP Techniques** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mmVzQBW_2M_8"
      },
      "source": [
        "### **Importing Libraries**"
      ]
    },
    {
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "IZaqOojK2M_9",
        "outputId": "3ec6b30e-e654-44d6-fca2-c9cf4a062432"
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          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "#importing libraries for reading csv\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import nltk\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.corpus import stopwords\n",
        "from gensim.models import Word2Vec\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ej8Uc4Zr2M__"
      },
      "source": [
        "### **Reading Datatset**"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
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        "id": "qPB_zCfI2M__",
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Label</th>\n",
              "      <th>Top1</th>\n",
              "      <th>Top2</th>\n",
              "      <th>Top3</th>\n",
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              "      <th>...</th>\n",
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              "      <th>Top20</th>\n",
              "      <th>Top21</th>\n",
              "      <th>Top22</th>\n",
              "      <th>Top23</th>\n",
              "      <th>Top24</th>\n",
              "      <th>Top25</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2000-01-03</td>\n",
              "      <td>0</td>\n",
              "      <td>A 'hindrance to operations': extracts from the...</td>\n",
              "      <td>Scorecard</td>\n",
              "      <td>Hughes' instant hit buoys Blues</td>\n",
              "      <td>Jack gets his skates on at ice-cold Alex</td>\n",
              "      <td>Chaos as Maracana builds up for United</td>\n",
              "      <td>Depleted Leicester prevail as Elliott spoils E...</td>\n",
              "      <td>Hungry Spurs sense rich pickings</td>\n",
              "      <td>Gunners so wide of an easy target</td>\n",
              "      <td>...</td>\n",
              "      <td>Flintoff injury piles on woe for England</td>\n",
              "      <td>Hunters threaten Jospin with new battle of the...</td>\n",
              "      <td>Kohl's successor drawn into scandal</td>\n",
              "      <td>The difference between men and women</td>\n",
              "      <td>Sara Denver, nurse turned solicitor</td>\n",
              "      <td>Diana's landmine crusade put Tories in a panic</td>\n",
              "      <td>Yeltsin's resignation caught opposition flat-f...</td>\n",
              "      <td>Russian roulette</td>\n",
              "      <td>Sold out</td>\n",
              "      <td>Recovering a title</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2000-01-04</td>\n",
              "      <td>0</td>\n",
              "      <td>Scorecard</td>\n",
              "      <td>The best lake scene</td>\n",
              "      <td>Leader: German sleaze inquiry</td>\n",
              "      <td>Cheerio, boyo</td>\n",
              "      <td>The main recommendations</td>\n",
              "      <td>Has Cubie killed fees?</td>\n",
              "      <td>Has Cubie killed fees?</td>\n",
              "      <td>Has Cubie killed fees?</td>\n",
              "      <td>...</td>\n",
              "      <td>On the critical list</td>\n",
              "      <td>The timing of their lives</td>\n",
              "      <td>Dear doctor</td>\n",
              "      <td>Irish court halts IRA man's extradition to Nor...</td>\n",
              "      <td>Burundi peace initiative fades after rebels re...</td>\n",
              "      <td>PE points the way forward to the ECB</td>\n",
              "      <td>Campaigners keep up pressure on Nazi war crime...</td>\n",
              "      <td>Jane Ratcliffe</td>\n",
              "      <td>Yet more things you wouldn't know without the ...</td>\n",
              "      <td>Millennium bug fails to bite</td>\n",
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              "  </tbody>\n",
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              "<p>2 rows × 27 columns</p>\n",
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              "         Date  Label                                               Top1  \\\n",
              "0  2000-01-03      0  A 'hindrance to operations': extracts from the...   \n",
              "1  2000-01-04      0                                          Scorecard   \n",
              "\n",
              "                  Top2                             Top3  \\\n",
              "0            Scorecard  Hughes' instant hit buoys Blues   \n",
              "1  The best lake scene    Leader: German sleaze inquiry   \n",
              "\n",
              "                                       Top4  \\\n",
              "0  Jack gets his skates on at ice-cold Alex   \n",
              "1                             Cheerio, boyo   \n",
              "\n",
              "                                     Top5  \\\n",
              "0  Chaos as Maracana builds up for United   \n",
              "1                The main recommendations   \n",
              "\n",
              "                                                Top6  \\\n",
              "0  Depleted Leicester prevail as Elliott spoils E...   \n",
              "1                             Has Cubie killed fees?   \n",
              "\n",
              "                               Top7                               Top8  ...  \\\n",
              "0  Hungry Spurs sense rich pickings  Gunners so wide of an easy target  ...   \n",
              "1            Has Cubie killed fees?             Has Cubie killed fees?  ...   \n",
              "\n",
              "                                      Top16  \\\n",
              "0  Flintoff injury piles on woe for England   \n",
              "1                      On the critical list   \n",
              "\n",
              "                                               Top17  \\\n",
              "0  Hunters threaten Jospin with new battle of the...   \n",
              "1                          The timing of their lives   \n",
              "\n",
              "                                 Top18  \\\n",
              "0  Kohl's successor drawn into scandal   \n",
              "1                          Dear doctor   \n",
              "\n",
              "                                               Top19  \\\n",
              "0               The difference between men and women   \n",
              "1  Irish court halts IRA man's extradition to Nor...   \n",
              "\n",
              "                                               Top20  \\\n",
              "0                Sara Denver, nurse turned solicitor   \n",
              "1  Burundi peace initiative fades after rebels re...   \n",
              "\n",
              "                                            Top21  \\\n",
              "0  Diana's landmine crusade put Tories in a panic   \n",
              "1            PE points the way forward to the ECB   \n",
              "\n",
              "                                               Top22             Top23  \\\n",
              "0  Yeltsin's resignation caught opposition flat-f...  Russian roulette   \n",
              "1  Campaigners keep up pressure on Nazi war crime...    Jane Ratcliffe   \n",
              "\n",
              "                                               Top24  \\\n",
              "0                                           Sold out   \n",
              "1  Yet more things you wouldn't know without the ...   \n",
              "\n",
              "                          Top25  \n",
              "0            Recovering a title  \n",
              "1  Millennium bug fails to bite  \n",
              "\n",
              "[2 rows x 27 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#reading our dataset\n",
        "df = pd.read_csv(r\"Stock News Dataset.csv\",encoding = \"ISO-8859-1\")\n",
        "df.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "radyeCzA2M__"
      },
      "source": [
        "### **Shape of the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZJGrkl_2M__",
        "outputId": "d231649d-9d35-4977-80fe-10f97cb2ac12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Rows and Columns of the dataset :-  (4101, 27)\n"
          ]
        }
      ],
      "source": [
        "#checking the number of rows and columns of the dataset\n",
        "print(\"Rows and Columns of the dataset :- \",df.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8iQWdURL2M__"
      },
      "source": [
        "### **Checking the distribution of target variable**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "3oJTN0nG2NAA",
        "outputId": "c29258f8-104c-4a47-d610-74d4a1832247"
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          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#plotting the target variable\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.countplot(x='Label', data=df)\n",
        "plt.xlabel('Label')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of the target variable')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "obtI8iRM2NAA"
      },
      "source": [
        "### **Dropping unwanted columns**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "j53mxqWA2NAA",
        "outputId": "0d11a1cc-63b6-4efa-b78e-83c17846c8b1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-558c29f6-fffd-4503-8e83-1495685a9b7b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Top1</th>\n",
              "      <th>Top2</th>\n",
              "      <th>Top3</th>\n",
              "      <th>Top4</th>\n",
              "      <th>Top5</th>\n",
              "      <th>Top6</th>\n",
              "      <th>Top7</th>\n",
              "      <th>Top8</th>\n",
              "      <th>Top9</th>\n",
              "      <th>...</th>\n",
              "      <th>Top16</th>\n",
              "      <th>Top17</th>\n",
              "      <th>Top18</th>\n",
              "      <th>Top19</th>\n",
              "      <th>Top20</th>\n",
              "      <th>Top21</th>\n",
              "      <th>Top22</th>\n",
              "      <th>Top23</th>\n",
              "      <th>Top24</th>\n",
              "      <th>Top25</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>A 'hindrance to operations': extracts from the...</td>\n",
              "      <td>Scorecard</td>\n",
              "      <td>Hughes' instant hit buoys Blues</td>\n",
              "      <td>Jack gets his skates on at ice-cold Alex</td>\n",
              "      <td>Chaos as Maracana builds up for United</td>\n",
              "      <td>Depleted Leicester prevail as Elliott spoils E...</td>\n",
              "      <td>Hungry Spurs sense rich pickings</td>\n",
              "      <td>Gunners so wide of an easy target</td>\n",
              "      <td>Derby raise a glass to Strupar's debut double</td>\n",
              "      <td>...</td>\n",
              "      <td>Flintoff injury piles on woe for England</td>\n",
              "      <td>Hunters threaten Jospin with new battle of the...</td>\n",
              "      <td>Kohl's successor drawn into scandal</td>\n",
              "      <td>The difference between men and women</td>\n",
              "      <td>Sara Denver, nurse turned solicitor</td>\n",
              "      <td>Diana's landmine crusade put Tories in a panic</td>\n",
              "      <td>Yeltsin's resignation caught opposition flat-f...</td>\n",
              "      <td>Russian roulette</td>\n",
              "      <td>Sold out</td>\n",
              "      <td>Recovering a title</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Scorecard</td>\n",
              "      <td>The best lake scene</td>\n",
              "      <td>Leader: German sleaze inquiry</td>\n",
              "      <td>Cheerio, boyo</td>\n",
              "      <td>The main recommendations</td>\n",
              "      <td>Has Cubie killed fees?</td>\n",
              "      <td>Has Cubie killed fees?</td>\n",
              "      <td>Has Cubie killed fees?</td>\n",
              "      <td>Hopkins 'furious' at Foster's lack of Hannibal...</td>\n",
              "      <td>...</td>\n",
              "      <td>On the critical list</td>\n",
              "      <td>The timing of their lives</td>\n",
              "      <td>Dear doctor</td>\n",
              "      <td>Irish court halts IRA man's extradition to Nor...</td>\n",
              "      <td>Burundi peace initiative fades after rebels re...</td>\n",
              "      <td>PE points the way forward to the ECB</td>\n",
              "      <td>Campaigners keep up pressure on Nazi war crime...</td>\n",
              "      <td>Jane Ratcliffe</td>\n",
              "      <td>Yet more things you wouldn't know without the ...</td>\n",
              "      <td>Millennium bug fails to bite</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-558c29f6-fffd-4503-8e83-1495685a9b7b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  "
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              "   Label                                               Top1  \\\n",
              "0      0  A 'hindrance to operations': extracts from the...   \n",
              "1      0                                          Scorecard   \n",
              "\n",
              "                  Top2                             Top3  \\\n",
              "0            Scorecard  Hughes' instant hit buoys Blues   \n",
              "1  The best lake scene    Leader: German sleaze inquiry   \n",
              "\n",
              "                                       Top4  \\\n",
              "0  Jack gets his skates on at ice-cold Alex   \n",
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              "\n",
              "                                     Top5  \\\n",
              "0  Chaos as Maracana builds up for United   \n",
              "1                The main recommendations   \n",
              "\n",
              "                                                Top6  \\\n",
              "0  Depleted Leicester prevail as Elliott spoils E...   \n",
              "1                             Has Cubie killed fees?   \n",
              "\n",
              "                               Top7                               Top8  \\\n",
              "0  Hungry Spurs sense rich pickings  Gunners so wide of an easy target   \n",
              "1            Has Cubie killed fees?             Has Cubie killed fees?   \n",
              "\n",
              "                                                Top9  ...  \\\n",
              "0      Derby raise a glass to Strupar's debut double  ...   \n",
              "1  Hopkins 'furious' at Foster's lack of Hannibal...  ...   \n",
              "\n",
              "                                      Top16  \\\n",
              "0  Flintoff injury piles on woe for England   \n",
              "1                      On the critical list   \n",
              "\n",
              "                                               Top17  \\\n",
              "0  Hunters threaten Jospin with new battle of the...   \n",
              "1                          The timing of their lives   \n",
              "\n",
              "                                 Top18  \\\n",
              "0  Kohl's successor drawn into scandal   \n",
              "1                          Dear doctor   \n",
              "\n",
              "                                               Top19  \\\n",
              "0               The difference between men and women   \n",
              "1  Irish court halts IRA man's extradition to Nor...   \n",
              "\n",
              "                                               Top20  \\\n",
              "0                Sara Denver, nurse turned solicitor   \n",
              "1  Burundi peace initiative fades after rebels re...   \n",
              "\n",
              "                                            Top21  \\\n",
              "0  Diana's landmine crusade put Tories in a panic   \n",
              "1            PE points the way forward to the ECB   \n",
              "\n",
              "                                               Top22             Top23  \\\n",
              "0  Yeltsin's resignation caught opposition flat-f...  Russian roulette   \n",
              "1  Campaigners keep up pressure on Nazi war crime...    Jane Ratcliffe   \n",
              "\n",
              "                                               Top24  \\\n",
              "0                                           Sold out   \n",
              "1  Yet more things you wouldn't know without the ...   \n",
              "\n",
              "                          Top25  \n",
              "0            Recovering a title  \n",
              "1  Millennium bug fails to bite  \n",
              "\n",
              "[2 rows x 26 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# dropping the unwanted columns\n",
        "df = df.drop('Date',axis=1)\n",
        "df.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qva3RmrT2NAA"
      },
      "source": [
        "### **Merging the Columns**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "1m9uhu2B2NAB",
        "outputId": "eddc7f05-4a18-4f19-bffb-845a9bcb20b3"
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      "outputs": [
        {
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              "2    0\n",
              "3    1\n",
              "4    1\n",
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          "execution_count": 6,
          "metadata": {},
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      "source": [
        "df1=df.iloc[:,0]\n",
        "df1.head()"
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              "                                                Top1       Top2  \\\n",
              "0  A 'hindrance to operations': extracts from the...  Scorecard   \n",
              "\n",
              "                              Top3                                      Top4  \\\n",
              "0  Hughes' instant hit buoys Blues  Jack gets his skates on at ice-cold Alex   \n",
              "\n",
              "                                     Top5  \\\n",
              "0  Chaos as Maracana builds up for United   \n",
              "\n",
              "                                                Top6  \\\n",
              "0  Depleted Leicester prevail as Elliott spoils E...   \n",
              "\n",
              "                               Top7                               Top8  \\\n",
              "0  Hungry Spurs sense rich pickings  Gunners so wide of an easy target   \n",
              "\n",
              "                                            Top9  \\\n",
              "0  Derby raise a glass to Strupar's debut double   \n",
              "\n",
              "                                      Top10  ...  \\\n",
              "0  Southgate strikes, Leeds pay the penalty  ...   \n",
              "\n",
              "                                      Top16  \\\n",
              "0  Flintoff injury piles on woe for England   \n",
              "\n",
              "                                               Top17  \\\n",
              "0  Hunters threaten Jospin with new battle of the...   \n",
              "\n",
              "                                 Top18                                 Top19  \\\n",
              "0  Kohl's successor drawn into scandal  The difference between men and women   \n",
              "\n",
              "                                 Top20  \\\n",
              "0  Sara Denver, nurse turned solicitor   \n",
              "\n",
              "                                            Top21  \\\n",
              "0  Diana's landmine crusade put Tories in a panic   \n",
              "\n",
              "                                               Top22             Top23  \\\n",
              "0  Yeltsin's resignation caught opposition flat-f...  Russian roulette   \n",
              "\n",
              "      Top24               Top25  \n",
              "0  Sold out  Recovering a title  \n",
              "\n",
              "[1 rows x 25 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
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        "df2=df.iloc[:,1:26]\n",
        "df2.head(1)"
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      "execution_count": null,
      "metadata": {
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        "id": "IP7wmQSo2NAB",
        "outputId": "31f6f460-ece7-4d4d-b6f7-a551f45a40c7"
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        {
          "data": {
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              "0       A 'hindrance to operations': extracts from the...\n",
              "1       Scorecard The best lake scene Leader: German s...\n",
              "2       Coventry caught on counter by Flo United's riv...\n",
              "3       Pilgrim knows how to progress Thatcher facing ...\n",
              "4       Hitches and Horlocks Beckham off but United su...\n",
              "                              ...                        \n",
              "4096    Barclays and RBS shares suspended from trading...\n",
              "4097    2,500 Scientists To Australia: If You Want To ...\n",
              "4098    Explosion At Airport In Istanbul Yemeni former...\n",
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              "4100    A 117-year-old woman in Mexico City finally re...\n",
              "Name: headlines, Length: 4101, dtype: object"
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          "execution_count": 8,
          "metadata": {},
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      "source": [
        "# Merge all 25 columns into a single column\n",
        "df2['headlines'] = df2.apply(lambda x: ' '.join(x.astype(str)), axis=1)\n",
        "df2['headlines']"
      ]
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              "<p>4101 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4b86280-59c5-4a7a-8475-a319c3cc73c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f4b86280-59c5-4a7a-8475-a319c3cc73c4 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-f4b86280-59c5-4a7a-8475-a319c3cc73c4');\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",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Label                                          headlines\n",
              "0         0  A 'hindrance to operations': extracts from the...\n",
              "1         0  Scorecard The best lake scene Leader: German s...\n",
              "2         0  Coventry caught on counter by Flo United's riv...\n",
              "3         1  Pilgrim knows how to progress Thatcher facing ...\n",
              "4         1  Hitches and Horlocks Beckham off but United su...\n",
              "...     ...                                                ...\n",
              "4096      0  Barclays and RBS shares suspended from trading...\n",
              "4097      1  2,500 Scientists To Australia: If You Want To ...\n",
              "4098      1  Explosion At Airport In Istanbul Yemeni former...\n",
              "4099      1  Jamaica proposes marijuana dispensers for tour...\n",
              "4100      1  A 117-year-old woman in Mexico City finally re...\n",
              "\n",
              "[4101 rows x 2 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Concatenate the two dataframes vertically\n",
        "data = pd.concat([df1, df2['headlines']],axis=1)\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RgTh8-Mk2NAC"
      },
      "source": [
        "### **NLP Pre-Processing**  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "eoW51RDu2NAC",
        "outputId": "2388e8e7-1d72-4cb2-c656-dc413d52204b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-10-234ed52a87d3>:2: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  data['headlines'] = data['headlines'].str.replace(r'\\d+(\\.\\d+)?', 'numbers')\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ee39b7d2-c644-41b4-9070-a016a71b187e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>headlines</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>A 'hindrance to operations': extracts from the...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Scorecard The best lake scene Leader: German s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>Coventry caught on counter by Flo United's riv...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Pilgrim knows how to progress Thatcher facing ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Hitches and Horlocks Beckham off but United su...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee39b7d2-c644-41b4-9070-a016a71b187e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ee39b7d2-c644-41b4-9070-a016a71b187e 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-ee39b7d2-c644-41b4-9070-a016a71b187e');\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",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Label                                          headlines\n",
              "0      0  A 'hindrance to operations': extracts from the...\n",
              "1      0  Scorecard The best lake scene Leader: German s...\n",
              "2      0  Coventry caught on counter by Flo United's riv...\n",
              "3      1  Pilgrim knows how to progress Thatcher facing ...\n",
              "4      1  Hitches and Horlocks Beckham off but United su..."
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# removing numbers \n",
        "data['headlines'] = data['headlines'].str.replace(r'\\d+(\\.\\d+)?', 'numbers')\n",
        "data.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bmAFWOzC2NAC",
        "outputId": "4f820844-32e7-44af-b21d-e28e5f79a417"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b7e3910d-cbdc-49a3-9c25-48f41207429c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>headlines</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>a 'hindrance to operations': extracts from the...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>scorecard the best lake scene leader: german s...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>coventry caught on counter by flo united's riv...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>pilgrim knows how to progress thatcher facing ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>hitches and horlocks beckham off but united su...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7e3910d-cbdc-49a3-9c25-48f41207429c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b7e3910d-cbdc-49a3-9c25-48f41207429c button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-b7e3910d-cbdc-49a3-9c25-48f41207429c');\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",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Label                                          headlines\n",
              "0      0  a 'hindrance to operations': extracts from the...\n",
              "1      0  scorecard the best lake scene leader: german s...\n",
              "2      0  coventry caught on counter by flo united's riv...\n",
              "3      1  pilgrim knows how to progress thatcher facing ...\n",
              "4      1  hitches and horlocks beckham off but united su..."
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Converting into lowercase\n",
        "data['headlines'] = data['headlines'].str.lower()\n",
        "data.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OMxdVzuq2NAC",
        "outputId": "bfab496c-063f-40bb-b0d8-a260e4b23d27"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-12-a8d7e46492e3>:2: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  data['headlines'] = data['headlines'].str.replace(r'\\n',\" \")\n",
            "<ipython-input-12-a8d7e46492e3>:5: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  data['headlines'] = data['headlines'].str.replace(r'£|\\$', 'money')\n",
            "<ipython-input-12-a8d7e46492e3>:8: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  data['headlines'] = data['headlines'].str.replace(r'\\s+', ' ')\n",
            "<ipython-input-12-a8d7e46492e3>:11: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  data['headlines'] = data['headlines'].str.replace(r\"[^a-zA-Z0-9]+\", \" \")\n"
          ]
        }
      ],
      "source": [
        "#Replacing next line by a 'white space'\n",
        "data['headlines'] = data['headlines'].str.replace(r'\\n',\" \") \n",
        "\n",
        "# Replacing currency sign by 'money'\n",
        "data['headlines'] = data['headlines'].str.replace(r'£|\\$', 'money')\n",
        "\n",
        "# Replacing large white space by single white space\n",
        "data['headlines'] = data['headlines'].str.replace(r'\\s+', ' ')\n",
        "\n",
        "# Replacing special characters by white space\n",
        "data['headlines'] = data['headlines'].str.replace(r\"[^a-zA-Z0-9]+\", \" \")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "9rzy4CA52NAD",
        "outputId": "e4618448-34da-47d2-b798-5eee85a5c35e"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>headlines</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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            ],
            "text/plain": [
              "   Label                                          headlines\n",
              "0      0  a hindrance to operations extracts from the le...\n",
              "1      0  scorecard the best lake scene leader german sl...\n",
              "2      0  coventry caught on counter by flo united s riv...\n",
              "3      1  pilgrim knows how to progress thatcher facing ...\n",
              "4      1  hitches and horlocks beckham off but united su..."
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLoAF4AoTUMu"
      },
      "source": [
        "### **Tokenization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D8ktks772NAD"
      },
      "outputs": [],
      "source": [
        "# tokenizing the documents\n",
        "list_array = []\n",
        "for i in range(len(data['headlines'])):\n",
        "  temp = data['headlines'][i].split(' ')\n",
        "  list_array.append(temp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVYlAOZC2NAD",
        "outputId": "28a6123f-c467-4022-f519-e132eea06eb9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-15-764616f2eb65>:2: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  list_array = np.array(list_array)\n"
          ]
        }
      ],
      "source": [
        "# converting list_array into numpy array\n",
        "list_array = np.array(list_array)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XwxPzso2NAD"
      },
      "source": [
        "### **Stemming & Stopward Removal**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0-M2hN5k2NAE"
      },
      "outputs": [],
      "source": [
        "stemmer = PorterStemmer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Iqe1xxpS2NAE"
      },
      "outputs": [],
      "source": [
        "# performing stemming & stop word removal on the tokenized words \n",
        "for i  in range(len(list_array)):\n",
        "  words = [stemmer.stem(word) for word in list_array[i] if word not in set(stopwords.words('english'))]\n",
        "  list_array[i] = words"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQn-_h4n2NAE"
      },
      "source": [
        "### **Lemmatization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7YUIjlWP2NAE"
      },
      "outputs": [],
      "source": [
        "# performing lemmatizing \n",
        "list_headlines = []\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "for i in range(len(list_array)):\n",
        "  words = [lemmatizer.lemmatize(word) for word in list_array[i]]\n",
        "  words = ' '.join(words)\n",
        "  list_headlines.append(words)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oUK1cDVq2NAE"
      },
      "outputs": [],
      "source": [
        "list_headlines = list(list_headlines)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "u89HKjVh2NAE",
        "outputId": "99e0e35e-351c-487b-9039-98ffa1c2e1b3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'hindranc oper extract leak report scorecard hugh instant hit buoy blue jack get skate ice cold alex chao maracana build unit deplet leicest prevail elliott spoil everton parti hungri spur sen rich pick gunner wide easi target derbi rais glass strupar debut doubl southgat strike leed pay penalti hammer hand robson youth lesson saint parti like number wear wolv turn lamb stump mike catch testi gough taunt langer escap hit number flintoff injuri pile woe england hunter threaten jospin new battl somm kohl successor drawn scandal differ men woman sara denver nurs turn solicitor diana landmin crusad put torus panic yeltsin resign caught opposit flat foot russian roulett sold recov titl'"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_headlines[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J89iSER02NAE"
      },
      "source": [
        "### **Bag of Words**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XuozrOkb2NAF"
      },
      "outputs": [],
      "source": [
        "cv = CountVectorizer(max_features=50)\n",
        "Bow = cv.fit_transform(list_headlines).toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7zQoXFne2NAF",
        "outputId": "04a0b3d7-2dff-4191-fc2d-7b5d7c45ceb0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 1, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 4, 0, 1, 0,\n",
              "        1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 1,\n",
              "        0, 0, 1, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "        0, 1, 0, 1, 0, 0],\n",
              "       [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "        0, 0, 1, 0, 0, 0],\n",
              "       [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
              "        0, 0, 0, 0, 0, 0]])"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Bow[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABL6UyzS2NAF"
      },
      "source": [
        "### **Tf-Idf**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dv1o6KZ92NAF"
      },
      "outputs": [],
      "source": [
        "tf = TfidfVectorizer(max_features=50)\n",
        "tfidf = tf.fit_transform(list_headlines).toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWzxk_tA2NAF",
        "outputId": "c37d6b19-a6c6-46f2-c96b-d022fe2b4767"
      },
      "outputs": [
        {
          "data": {
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              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.29853651, 0.        , 0.42256644, 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.39458329,\n",
              "        0.        , 0.        , 0.        , 0.48424607, 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.41549203, 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "        0.        , 0.        , 0.22391141, 0.        , 0.        ,\n",
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              "        0.23511699, 0.        , 0.22935929, 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.32078914,\n",
              "        0.        , 0.        , 0.2140484 , 0.23382922, 0.        ,\n",
              "        0.        , 0.21772308, 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.37824854,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.43906626,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.37876915, 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.42161299, 0.        ,\n",
              "        0.42460381, 0.        , 0.40328263, 0.        , 0.        ],\n",
              "       [0.        , 0.31201916, 0.32630795, 0.        , 0.29697086,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.3232114 , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.34472013,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.22749971, 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.44916446, 0.        , 0.        , 0.36901965, 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.30821675, 0.        , 0.        , 0.        ],\n",
              "       [0.64193391, 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.34548521, 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.24317764, 0.38843994, 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.3651643 , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.35382894, 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ]])"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tfidf[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ohJZ4KrA2NAF"
      },
      "source": [
        "### **Word2Vec**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ky4S7Shz2NAF"
      },
      "outputs": [],
      "source": [
        "# performing lemmatizing \n",
        "lemmatizer = WordNetLemmatizer()\n",
        "for i in range(len(list_array)):\n",
        "  words = [lemmatizer.lemmatize(word) for word in list_array[i]]\n",
        "  list_array[i] = words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OznMuShZ2NAG"
      },
      "outputs": [],
      "source": [
        "word2vec = Word2Vec(list_array, min_count=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O_ZRLtxqIOt3",
        "outputId": "482e5324-2afc-4852-8583-3767fab3a416"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 0.8841289 ,  0.27691364, -0.4821399 ,  0.12342966, -0.44532195,\n",
              "       -0.26696378,  0.03501777,  0.39886886,  0.09552924, -0.30577675,\n",
              "       -0.23550442, -0.01636664,  0.24808261, -0.02717515,  0.1620689 ,\n",
              "        0.27343664,  0.04620099, -0.2388581 , -0.43883768,  0.5007952 ,\n",
              "        0.21729954,  0.20729189, -0.20368204, -0.7470815 , -0.25593612,\n",
              "        0.33404493,  0.1462208 , -0.11999304,  0.03724469, -0.1659818 ,\n",
              "        0.05668767,  0.30458936, -1.0046494 , -0.43588325,  0.4392221 ,\n",
              "        0.5408813 , -0.21510182,  0.11801285,  0.3425327 , -0.24928506,\n",
              "       -0.2579842 , -0.00563084, -0.5955832 ,  0.5659522 ,  0.14741412,\n",
              "        0.06762839,  0.42714298, -0.08357985,  0.2766211 ,  0.6617683 ,\n",
              "        0.4157613 , -0.10644194, -0.03293714,  0.21306755,  0.5730148 ,\n",
              "        0.35029316,  0.29320207, -0.33216307,  0.30449164,  0.12906387,\n",
              "        0.2765627 , -0.6542107 ,  0.14036651, -0.0282042 , -0.12286548,\n",
              "       -0.00183148,  0.14666028,  0.28052232, -0.15948778, -0.5010456 ,\n",
              "       -0.6755245 , -0.01581565, -0.08338265, -0.14751227, -0.10597102,\n",
              "       -0.57237905,  0.27182612,  0.41723263,  0.1263147 ,  0.18508929,\n",
              "        0.09482176,  0.33859232, -0.36758214, -0.1744736 ,  0.16518906,\n",
              "       -0.27290127, -0.15885577, -0.17400728,  0.426356  , -0.40605974,\n",
              "        0.08469434,  0.0082535 , -0.02412064,  0.06319845, -0.21599513,\n",
              "        0.17548546, -0.78693765, -0.22402063, -0.4805566 ,  0.01718105],\n",
              "      dtype=float32)"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "v1 = word2vec.wv['work']\n",
        "v1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxMUGJiRIQie",
        "outputId": "339c3f65-12f6-4900-8a1e-3f1069c3ac03"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('littl', 0.9407216310501099),\n",
              " ('pharma', 0.9400098323822021),\n",
              " ('person', 0.9339442253112793),\n",
              " ('zinc', 0.9322502613067627),\n",
              " ('better', 0.9302760362625122),\n",
              " ('alright', 0.9295676946640015),\n",
              " ('giveaway', 0.9280829429626465),\n",
              " ('thing', 0.9262166023254395),\n",
              " ('poo', 0.9243161678314209),\n",
              " ('seam', 0.9234308004379272)]"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sim_words = word2vec.wv.most_similar('work')\n",
        "sim_words"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQnIeL0a2NAG"
      },
      "source": [
        "### **Splitting the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-plKxK0L2NAG"
      },
      "outputs": [],
      "source": [
        "# Split into training and testing data\n",
        "x = Bow\n",
        "y = data['Label']\n",
        "x, x_test, y, y_test = train_test_split(x,y, stratify=y, test_size=0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mfkJXGxEBMEm"
      },
      "source": [
        "### **Implementing Multinomial Naive Bayes**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "GsdCuXuCGIyX",
        "outputId": "c3c2344b-a314-44f7-b5de-3943affbbff7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = MultinomialNB()\n",
        "model.fit(x, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0egsPxvGNoK",
        "outputId": "157c33c1-7d9b-40b5-b0ec-2babb42d82ad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.48927875243664715"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UTq2TqE9GQry"
      },
      "outputs": [],
      "source": [
        "# Split into training and testing data\n",
        "x = tfidf\n",
        "y = data['Label']\n",
        "x, x_test, y, y_test = train_test_split(x,y, stratify=y, test_size=0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "SUmzpB_CGTne",
        "outputId": "f3bea26c-2ef3-452a-f2fc-eb959c7e95ee"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = MultinomialNB()\n",
        "model.fit(x, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sf00W9AgGWVW",
        "outputId": "91d4eae5-dd13-451a-ca08-47db66f2599d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.5282651072124757"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bu_f04BbGo_A"
      },
      "source": [
        "### **Implementing Random Forest Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "M20zteFxAoPX",
        "outputId": "402a6774-ffcf-46c1-ad93-de88fa1139e1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# implement RandomForest Classifier\n",
        "model=RandomForestClassifier()\n",
        "model.fit(x,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "643tiNjPAyZA",
        "outputId": "223d8a10-d044-4d4c-ad3b-66fb85032942"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.5019493177387915"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.score(x_test,y_test)   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6BUjwEGTA_-_"
      },
      "outputs": [],
      "source": [
        "# Split into training and testing data\n",
        "x = tfidf\n",
        "y = data['Label']\n",
        "x, x_test, y, y_test = train_test_split(x,y, stratify=y, test_size=0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "BZr4UP93BAsI",
        "outputId": "8c2ab9c1-628e-43aa-c63e-851c6273454d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model=RandomForestClassifier()\n",
        "model.fit(x,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xhna2v5RBE4S",
        "outputId": "c3db31b1-0b86-418d-e0bd-abb78a22aefd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.5038986354775828"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.score(x_test,y_test)   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LdMUQHdhTmoI"
      },
      "source": [
        "### **POS-Tagging**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WxCCwYSWUZqf",
        "outputId": "905897c4-aa9a-4e0d-87ec-349da895a311"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "[nltk_data] Downloading package universal_tagset to /root/nltk_data...\n",
            "[nltk_data]   Package universal_tagset is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import nltk\n",
        "from nltk import word_tokenize\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('universal_tagset')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "K_OKdCB_WKMm",
        "outputId": "017fff61-3c8c-420e-c570-4cf68a353da8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'hindranc oper extract leak report scorecard hugh instant hit buoy blue jack get skate ice cold alex chao maracana build unit deplet leicest prevail elliott spoil everton parti hungri spur sen rich pick gunner wide easi target derbi rais glass strupar debut doubl southgat strike leed pay penalti hammer hand robson youth lesson saint parti like number wear wolv turn lamb stump mike catch testi gough taunt langer escap hit number flintoff injuri pile woe england hunter threaten jospin new battl somm kohl successor drawn scandal differ men woman sara denver nurs turn solicitor diana landmin crusad put torus panic yeltsin resign caught opposit flat foot russian roulett sold recov titl'"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_headlines[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TP8EAJqhVXJX"
      },
      "outputs": [],
      "source": [
        "tokens = word_tokenize(list_headlines[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UA2X0y00VcW7"
      },
      "outputs": [],
      "source": [
        "tags = nltk.pos_tag(tokens,tagset=\"universal\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hjAHESYQV61l",
        "outputId": "02029552-3d25-4b3e-bd61-ec4e113f5d09"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('hindranc', 'NOUN'),\n",
              " ('oper', 'ADP'),\n",
              " ('extract', 'ADJ'),\n",
              " ('leak', 'ADJ'),\n",
              " ('report', 'NOUN'),\n",
              " ('scorecard', 'VERB'),\n",
              " ('hugh', 'ADJ'),\n",
              " ('instant', 'NOUN'),\n",
              " ('hit', 'VERB'),\n",
              " ('buoy', 'ADJ'),\n",
              " ('blue', 'ADJ'),\n",
              " ('jack', 'NOUN'),\n",
              " ('get', 'NOUN'),\n",
              " ('skate', 'ADJ'),\n",
              " ('ice', 'NOUN'),\n",
              " ('cold', 'VERB'),\n",
              " ('alex', 'ADJ'),\n",
              " ('chao', 'NOUN'),\n",
              " ('maracana', 'X'),\n",
              " ('build', 'VERB'),\n",
              " ('unit', 'NOUN'),\n",
              " ('deplet', 'NOUN'),\n",
              " ('leicest', 'ADJ'),\n",
              " ('prevail', 'NOUN'),\n",
              " ('elliott', 'NOUN'),\n",
              " ('spoil', 'NOUN'),\n",
              " ('everton', 'NOUN'),\n",
              " ('parti', 'NOUN'),\n",
              " ('hungri', 'NOUN'),\n",
              " ('spur', 'VERB'),\n",
              " ('sen', 'NOUN'),\n",
              " ('rich', 'ADJ'),\n",
              " ('pick', 'NOUN'),\n",
              " ('gunner', 'NOUN'),\n",
              " ('wide', 'ADJ'),\n",
              " ('easi', 'NOUN'),\n",
              " ('target', 'NOUN'),\n",
              " ('derbi', 'NOUN'),\n",
              " ('rais', 'NOUN'),\n",
              " ('glass', 'NOUN'),\n",
              " ('strupar', 'NOUN'),\n",
              " ('debut', 'NOUN'),\n",
              " ('doubl', 'VERB'),\n",
              " ('southgat', 'VERB'),\n",
              " ('strike', 'NOUN'),\n",
              " ('leed', 'NOUN'),\n",
              " ('pay', 'NOUN'),\n",
              " ('penalti', 'NOUN'),\n",
              " ('hammer', 'NOUN'),\n",
              " ('hand', 'NOUN'),\n",
              " ('robson', 'NOUN'),\n",
              " ('youth', 'NOUN'),\n",
              " ('lesson', 'NOUN'),\n",
              " ('saint', 'NOUN'),\n",
              " ('parti', 'NOUN'),\n",
              " ('like', 'ADP'),\n",
              " ('number', 'NOUN'),\n",
              " ('wear', 'VERB'),\n",
              " ('wolv', 'ADJ'),\n",
              " ('turn', 'NOUN'),\n",
              " ('lamb', 'ADJ'),\n",
              " ('stump', 'NOUN'),\n",
              " ('mike', 'NOUN'),\n",
              " ('catch', 'VERB'),\n",
              " ('testi', 'NOUN'),\n",
              " ('gough', 'NOUN'),\n",
              " ('taunt', 'NOUN'),\n",
              " ('langer', 'NOUN'),\n",
              " ('escap', 'NOUN'),\n",
              " ('hit', 'VERB'),\n",
              " ('number', 'NOUN'),\n",
              " ('flintoff', 'NOUN'),\n",
              " ('injuri', 'NOUN'),\n",
              " ('pile', 'NOUN'),\n",
              " ('woe', 'NOUN'),\n",
              " ('england', 'VERB'),\n",
              " ('hunter', 'NOUN'),\n",
              " ('threaten', 'VERB'),\n",
              " ('jospin', 'ADJ'),\n",
              " ('new', 'ADJ'),\n",
              " ('battl', 'NOUN'),\n",
              " ('somm', 'NOUN'),\n",
              " ('kohl', 'NOUN'),\n",
              " ('successor', 'NOUN'),\n",
              " ('drawn', 'NOUN'),\n",
              " ('scandal', 'NOUN'),\n",
              " ('differ', 'VERB'),\n",
              " ('men', 'NOUN'),\n",
              " ('woman', 'NOUN'),\n",
              " ('sara', 'VERB'),\n",
              " ('denver', 'NOUN'),\n",
              " ('nurs', 'NOUN'),\n",
              " ('turn', 'VERB'),\n",
              " ('solicitor', 'ADJ'),\n",
              " ('diana', 'NOUN'),\n",
              " ('landmin', 'NOUN'),\n",
              " ('crusad', 'NOUN'),\n",
              " ('put', 'VERB'),\n",
              " ('torus', 'PRT'),\n",
              " ('panic', 'ADJ'),\n",
              " ('yeltsin', 'NOUN'),\n",
              " ('resign', 'NOUN'),\n",
              " ('caught', 'VERB'),\n",
              " ('opposit', 'ADV'),\n",
              " ('flat', 'ADJ'),\n",
              " ('foot', 'NOUN'),\n",
              " ('russian', 'ADJ'),\n",
              " ('roulett', 'NOUN'),\n",
              " ('sold', 'VERB'),\n",
              " ('recov', 'NOUN'),\n",
              " ('titl', 'NOUN')]"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tags "
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
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    "orig_nbformat": 4,
    "vscode": {
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