Fake_News_Classifier_Using_LSTM.ipynb

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  "metadata": {
    "colab": {
      "name": "Fake News Classifier Using LSTM.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNlx291+UgVpGh5lVkRYc+F",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sriprad/2017Spring/blob/master/Fake_News_Classifier_Using_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxMrNacR8nkL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLLZDp5n8pEG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eba1a7e4-fcbb-4263-cacb-c5baaee3b8a9"
      },
      "source": [
        "print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(-878.91626, shape=(), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-jmGns_583Tu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKmeBEg890-6",
        "colab_type": "code",
        "colab": {
          "resources": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "e363c07c-3ddf-4a01-bd1f-0e31b281af38"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c4e6f45f-56a7-4886-b6cd-d57ce5e44e46\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c4e6f45f-56a7-4886-b6cd-d57ce5e44e46\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving train.csv to train.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUc_VYAi91_B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import io\n",
        "df = pd.read_csv(io.BytesIO(uploaded['train.csv']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPoypOCVNWiy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "c500a1e0-3e5c-47d5-fc38-dbf19e45054c"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>What Keeps the F-35 Alive</td>\n",
              "      <td>David Swanson</td>\n",
              "      <td>David Swanson is an author, activist, journa...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20800 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  ... label\n",
              "0          0  ...     1\n",
              "1          1  ...     0\n",
              "2          2  ...     1\n",
              "3          3  ...     1\n",
              "4          4  ...     1\n",
              "...      ...  ...   ...\n",
              "20795  20795  ...     0\n",
              "20796  20796  ...     0\n",
              "20797  20797  ...     0\n",
              "20798  20798  ...     1\n",
              "20799  20799  ...     1\n",
              "\n",
              "[20800 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-h2wCk09YyT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df = pd.read_csv(\"C:/Users/P.SridharanSrinivas/Desktop/sai_pycaret/New_try/keras/NLP/train.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bVCbZUvSFui",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1 = df.copy()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuJZnCs-SQoh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1 = df1.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNrDkfD19fWN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "94ed28be-24d0-4f53-ee36-31ccc39d5876"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id           0\n",
              "title      558\n",
              "author    1957\n",
              "text        39\n",
              "label        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbLQYszIL1DC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "87c4e150-b80c-44b1-a43b-fccde4a45230"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>id</th>\n",
              "      <th>title</th>\n",
              "      <th>author</th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
              "      <td>Darrell Lucus</td>\n",
              "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>FLYNN: Hillary Clinton, Big Woman on Campus - ...</td>\n",
              "      <td>Daniel J. Flynn</td>\n",
              "      <td>Ever get the feeling your life circles the rou...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Why the Truth Might Get You Fired</td>\n",
              "      <td>Consortiumnews.com</td>\n",
              "      <td>Why the Truth Might Get You Fired October 29, ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>15 Civilians Killed In Single US Airstrike Hav...</td>\n",
              "      <td>Jessica Purkiss</td>\n",
              "      <td>Videos 15 Civilians Killed In Single US Airstr...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Iranian woman jailed for fictional unpublished...</td>\n",
              "      <td>Howard Portnoy</td>\n",
              "      <td>Print \\nAn Iranian woman has been sentenced to...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  ... label\n",
              "0   0  ...     1\n",
              "1   1  ...     0\n",
              "2   2  ...     1\n",
              "3   3  ...     1\n",
              "4   4  ...     1\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TxafaOmL_Eb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "6fa88693-6146-401e-f169-f5e6e94048c1"
      },
      "source": [
        "X = df1.drop('label',axis=1)\n",
        "print(df)\n",
        "# df1 = df.copy(deep=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "          id  ... label\n",
            "0          0  ...     1\n",
            "1          1  ...     0\n",
            "2          2  ...     1\n",
            "3          3  ...     1\n",
            "4          4  ...     1\n",
            "...      ...  ...   ...\n",
            "20795  20795  ...     0\n",
            "20796  20796  ...     0\n",
            "20797  20797  ...     0\n",
            "20798  20798  ...     1\n",
            "20799  20799  ...     1\n",
            "\n",
            "[20800 rows x 5 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6RFxnXIhSCZc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYB-KRccMBYD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y=df1['label']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJMfJqU_MR2M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "8cfb61fa-ab1f-4fdc-d6f5-f0359b55d73e"
      },
      "source": [
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1\n",
              "1        0\n",
              "2        1\n",
              "3        1\n",
              "4        1\n",
              "        ..\n",
              "20795    0\n",
              "20796    0\n",
              "20797    0\n",
              "20798    1\n",
              "20799    1\n",
              "Name: label, Length: 20800, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iRaNOOEhM1o5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e05a4cad-9e36-4ce2-b0eb-ed97db20a243"
      },
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'2.2.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQm484bBO3ae",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Embedding\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.preprocessing.text import one_hot\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CxHzmckPlcN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#voca size\n",
        "voc_size =5000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBMsVjgDPo5v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messages=X.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1O8TcgQP1ep",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messages.reset_index(inplace=True)\n",
        "# messages.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vS0r89PpP484",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "84b042f1-e611-4d77-a9d8-adde6053f96c"
      },
      "source": [
        "import nltk\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JfHI102vQAxb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "77e2adb4-f679-44f9-e42a-04f1b8facfb4"
      },
      "source": [
        "### Dataset Preprocessing\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "ps = PorterStemmer()\n",
        "corpus = []\n",
        "for i in range(0, len(messages)):\n",
        "    print(i)\n",
        "    review = re.sub('[^a-zA-Z]',' ',messages['title'][i])\n",
        "    review = review.lower()\n",
        "    review = review.split()\n",
        "    \n",
        "    review = [ps.stem(word) for word in review if not word in stopwords.words('english')]\n",
        "    review = ' '.join(review)\n",
        "    corpus.append(review)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "18284\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mF29-_QQG0b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 907
        },
        "outputId": "eedb8b35-34e4-4821-a4a0-e39a36ffca56"
      },
      "source": [
        "corpus"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hous dem aid even see comey letter jason chaffetz tweet',\n",
              " 'flynn hillari clinton big woman campu breitbart',\n",
              " 'truth might get fire',\n",
              " 'civilian kill singl us airstrik identifi',\n",
              " 'iranian woman jail fiction unpublish stori woman stone death adulteri',\n",
              " 'jacki mason hollywood would love trump bomb north korea lack tran bathroom exclus video breitbart',\n",
              " 'life life luxuri elton john favorit shark pictur stare long transcontinent flight',\n",
              " 'beno hamon win french socialist parti presidenti nomin new york time',\n",
              " 'excerpt draft script donald trump q ampa black church pastor new york time',\n",
              " 'back channel plan ukrain russia courtesi trump associ new york time',\n",
              " 'obama organ action partner soro link indivis disrupt trump agenda',\n",
              " 'bbc comedi sketch real housew isi caus outrag',\n",
              " 'russian research discov secret nazi militari base treasur hunter arctic photo',\n",
              " 'us offici see link trump russia',\n",
              " 'ye paid govern troll social media blog forum websit',\n",
              " 'major leagu soccer argentin find home success new york time',\n",
              " 'well fargo chief abruptli step new york time',\n",
              " 'anonym donor pay million releas everyon arrest dakota access pipelin',\n",
              " 'fbi close hillari',\n",
              " 'chuck todd buzzfe donald trump polit favor breitbart',\n",
              " 'news hope gop nude paul ryan emerg ayahuasca tent vision new republican parti',\n",
              " 'monica lewinski clinton sex scandal set american crime stori',\n",
              " 'rob reiner trump mental unstabl breitbart',\n",
              " 'massachusett cop wife bust pin fake home invas robberi black live matter',\n",
              " 'abort pill order rise latin american nation zika alert new york time',\n",
              " 'nuke un histor treati ban nuclear weapon',\n",
              " 'exclus islam state support vow shake west follow manchest terrorist massacr breitbart',\n",
              " 'humili hillari tri hide camera caught min ralli',\n",
              " 'andrea tantaro fox news claim retali sex harass complaint new york time',\n",
              " 'hillari clinton becam hawk new york time',\n",
              " 'chuck todd buzzfe eic publish fake news breitbart',\n",
              " 'israel becom pivot china mid eastern calculu http journal neo org israel becom pivot china mid eastern calculu',\n",
              " 'bori johnson brexit leader fumbl new york time',\n",
              " 'texa oil field rebound price lull job left behind new york time',\n",
              " 'bayer deal monsanto follow agribusi trend rais worri farmer new york time',\n",
              " 'russia move ban jehovah wit extremist new york time',\n",
              " 'still danger zone januari th',\n",
              " 'open thread u elect',\n",
              " 'democrat gutierrez blame chicago gun violenc nra breitbart',\n",
              " 'avoid peanut avoid allergi bad strategi new york time',\n",
              " 'mri show detail imag week unborn babi breitbart',\n",
              " 'best kind milk dairi',\n",
              " 'ryan locht drop speedo usa retail new york time',\n",
              " 'one girlfriend without bastard f king ask harri',\n",
              " 'conserv urg session clean obama civil right divis breitbart',\n",
              " 'intern inquiri seal fate roger ail fox new york time',\n",
              " 'press tv debat duff lebanon hezbollah aoun presid video',\n",
              " '',\n",
              " 'samsung combust galaxi note unveil new smartphon new york time',\n",
              " 'poland vow referendum migrant quota amidst eu pressur public voic heard breitbart',\n",
              " 'spark inner revolut',\n",
              " 'duel drill increas nato russia tension',\n",
              " 'studi half car crash involv driver distract cell phone breitbart']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMqQ2C1wQeBQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "57b89681-d921-4709-c229-7d1b7ff7312f"
      },
      "source": [
        "onehot_repr =[one_hot(words,voc_size)for words in corpus]\n",
        "onehot_repr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " [1950, 1122, 2634, 147, 1668, 3461, 2688, 1356],\n",
              " [4525, 3584, 856, 3651, 3370, 4329, 2917],\n",
              " [4802, 4780, 2650, 1988, 2603],\n",
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              " [2549, 2561, 4074, 1752, 1259, 3523, 355, 447, 114, 4234],\n",
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              " [4153, 4435, 1667, 2036, 304, 1793, 1577, 985, 327],\n",
              " [1472, 2003, 3832, 4757, 1717, 911],\n",
              " [2489, 658, 1307, 4784, 2748, 4536, 1184, 2206],\n",
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              "  3651],\n",
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              " [1510, 1727, 4042, 2739, 4891, 4146, 4918, 2145, 3105, 2857, 3651],\n",
              " [948, 3937, 1137, 2766, 1616, 1819, 1184, 1120],\n",
              " [2561, 4170, 1640, 4564, 1278, 2178, 4946, 3461, 2688, 1356],\n",
              " [4059, 1365, 4106, 3495, 6],\n",
              " [2201, 4133, 863, 3461, 2688, 1356],\n",
              " [889, 3372, 3160, 320, 1454],\n",
              " [2559, 4153, 554, 4933, 4316, 3878, 4603, 1893, 1698, 2561, 2664],\n",
              " [1083, 1622, 2721, 4779, 4597, 1341, 346, 4952, 3461, 2688, 1356],\n",
              " [4049, 2042, 1752, 1617, 3461, 757, 1740, 2565, 282],\n",
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              " [575, 3820, 4469, 1541, 893, 3575],\n",
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              "  1356],\n",
              " [1291, 3723, 2266, 4984, 4490, 1616, 4697, 2455, 1978, 1798],\n",
              " [2976, 4084, 4262, 3121, 1112, 2035, 4531, 1588, 1616, 3651],\n",
              " [2325, 2772, 3172, 2742, 3462, 2748, 4934],\n",
              " [3235, 220, 3491, 1726, 4032, 4030, 3264, 127, 1553, 3461, 2688, 1356],\n",
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              " [1438, 3089, 3461, 2688, 1356, 2178, 3651, 1307, 2999, 3651],\n",
              " [4974, 3461, 4488, 2466, 3366, 2631, 3461, 2688, 1356],\n",
              " [1970, 880, 57, 4271, 3732, 1083, 475, 1601, 3903],\n",
              " [3145, 492, 173, 1083, 1543, 4358, 2707, 2097, 3461, 2688, 1356],\n",
              " [1307, 1126, 1877, 3706, 4754],\n",
              " [1570, 1282, 1307, 4978, 1438, 3089, 2917, 1359, 554],\n",
              " [2994, 334, 2033, 3637, 2134, 2401, 1246, 3151, 2941, 3461, 2688, 1356],\n",
              " [124, 2648, 424, 1563, 3618, 3392, 1102, 2781],\n",
              " [2439, 3461, 437, 1165, 2151, 1752, 1016, 1049, 2178, 205, 4313, 2582, 114],\n",
              " [2994, 1005, 977, 2489, 1356, 1723, 1345, 2847, 388, 3461, 2688, 1356],\n",
              " [3661, 4531, 566, 1614, 2664, 1015, 867],\n",
              " [931, 2434, 3446, 1046],\n",
              " [3917, 1122, 1634, 4581, 2152, 2930, 1348, 3461, 2688, 1356],\n",
              " [3378, 1660, 4222, 2041],\n",
              " [4466, 781, 2226, 2178, 356, 2987, 1397, 4343],\n",
              " [2354, 1203, 3785, 2036, 920, 2874, 3461, 2688, 1356],\n",
              " [3811, 2853, 1556, 4531, 3584, 2201, 961, 818],\n",
              " [3920, 1142, 4603, 4105, 1126, 2400, 3374, 4793, 2561, 3651],\n",
              " [2417, 3261, 818, 1734, 2397, 3672, 4736, 4272, 1534, 4878, 3651],\n",
              " [1405, 1135, 4608, 4932, 4909, 3404, 3619, 2707, 3461, 2688, 1356],\n",
              " [3479, 18, 3308, 191, 2664, 3219, 2406],\n",
              " [4913, 2066, 2320, 4545, 666, 29],\n",
              " [1268, 2523, 1021, 2693, 1135],\n",
              " [4865, 529, 140, 1380, 266, 1654, 1256, 661, 4866, 3744, 3651],\n",
              " [1083, 67, 2781, 1122, 1083, 645, 2882, 4977, 856, 3461, 2688, 1356],\n",
              " [2561, 3329],\n",
              " [2786, 308, 3850, 526, 3733, 2217, 3633, 803, 3461, 2688, 1356],\n",
              " [2561, 3882, 781, 3790, 340, 1126, 3556, 3575, 3372, 4018],\n",
              " [391, 1667, 180, 3661, 4583, 1487, 1356, 3865, 3461, 2688, 1356],\n",
              " [2853, 993, 937, 2137, 1573, 972, 1617, 4554, 4531, 1353],\n",
              " [2639, 3968, 3749, 2266, 3454, 481, 818],\n",
              " [4180, 2292, 2449, 1525, 4617, 1224, 2197, 4839, 3461, 2688, 1356],\n",
              " [2456, 1147, 4148, 4873, 2666],\n",
              " [1322, 4018, 4482, 2805, 664, 3524, 1359],\n",
              " [325, 3461, 2141, 1001, 4715, 2914, 3718, 3461, 2688, 1356],\n",
              " [554, 4683, 1937, 4547, 44],\n",
              " [1359, 3837, 3477, 1725, 742, 2917, 2561, 3651],\n",
              " [3849, 726, 147, 2829, 432, 2263, 2769, 2201],\n",
              " [3144, 1254, 4280, 3423, 304, 3945, 2914, 2417],\n",
              " [1112, 4755, 4131, 3966, 2839, 1602],\n",
              " [4597, 310, 4762, 3691, 4133, 4957, 1030, 856, 1454, 3651],\n",
              " [3542, 1916, 1965, 2501, 515, 834, 3461, 2688, 1356],\n",
              " [3661, 4932, 3727, 917, 435, 3947, 390, 3461, 2688, 1356],\n",
              " [4685, 2523, 570, 3658, 4133, 1171, 3760, 3110],\n",
              " [3134, 980, 3716, 515, 1232, 889, 1359, 4679, 1686],\n",
              " [2417, 4161, 394, 4424, 524, 4655, 3461, 2688, 1356],\n",
              " [2235, 4284, 3419, 1574, 2578, 4424, 1239],\n",
              " [2473, 2106, 2533, 554, 3202],\n",
              " [1, 3062, 1058, 2930, 1803, 2206, 253, 2709],\n",
              " [3718, 856, 155, 2561, 1723, 2857, 3461, 2688, 1356],\n",
              " [2124, 1796, 4909, 3062, 781, 3641, 352, 538, 3944, 4018],\n",
              " [1438, 3089, 2561, 2914, 916, 623],\n",
              " [4265, 2627],\n",
              " [1585, 1224, 260, 2917, 404, 2627, 2400, 3651],\n",
              " [2237, 2917, 2297, 3461, 2688, 1356],\n",
              " [3793, 846, 2840, 726, 3022, 2403, 918, 2135, 539, 3974, 4603, 2077],\n",
              " [1151, 3966, 2649, 3052],\n",
              " [1683, 2550, 4933, 664, 1015, 1058, 3661, 3750],\n",
              " [4479, 742, 380, 1900, 38, 321, 163, 1673],\n",
              " [612, 3499, 4753, 3047, 3423, 1099, 1028, 1359],\n",
              " [1367, 2607, 2266, 241, 856, 1067, 3461, 2688, 1356],\n",
              " [1725, 57, 327, 113, 1300, 327, 1425, 742, 3651],\n",
              " [2343, 4549, 3849, 1755, 430, 2802, 4692, 1532, 4197, 4871],\n",
              " [4424, 597, 1356, 4360, 1420, 2561, 2969, 2589, 3651],\n",
              " [2441, 3920, 1737, 57, 922, 922, 3461, 2688, 1356],\n",
              " [1359, 554, 2198, 1667, 2561, 3461, 3657, 4096, 4603, 551, 3461, 2688, 1356],\n",
              " [3403, 4088, 3524, 2161, 1667, 2561, 1364, 2748, 1147, 3461, 2688, 1356],\n",
              " [3739, 2041, 3706, 977, 2489, 1793, 4103, 3651],\n",
              " [1324, 3505, 2992, 3303, 183, 4494, 3461, 2688, 1356],\n",
              " [10, 742, 2534, 1907, 4499, 4042, 3461, 2688, 1356],\n",
              " [4059, 4218, 4099],\n",
              " [3677, 1667, 2561, 1572, 4984, 2217, 3556, 4994, 1185, 3461, 2688, 1356],\n",
              " [2872, 1534, 817, 2810, 335, 59, 3461, 2688, 1356],\n",
              " [4467, 3280, 416, 4973, 2093, 123, 4355, 3873, 3402, 3651],\n",
              " [2561, 1420, 4611, 174, 1585, 3873, 734, 4265, 3803, 3732],\n",
              " [2872, 1520, 4791, 3725, 4736, 2003, 1614, 3461, 2688, 1356],\n",
              " [4753, 160, 608, 3727, 2734, 1015, 3807, 1482, 3363, 4679, 3651],\n",
              " [1496, 3619, 3708, 1612, 3280, 1184, 516, 3651],\n",
              " [1558,\n",
              "  3462,\n",
              "  3461,\n",
              "  253,\n",
              "  1786,\n",
              "  456,\n",
              "  443,\n",
              "  3210,\n",
              "  3120,\n",
              "  3264,\n",
              "  854,\n",
              "  3461,\n",
              "  2688,\n",
              "  1356],\n",
              " [1667, 2561, 1359, 554, 1347, 2196, 2748, 1147, 3461, 2688, 1356],\n",
              " [1058, 4133, 142, 1673, 1734, 335, 1075, 2448, 2473, 225],\n",
              " [4909, 4055, 2825, 2651, 1359, 2976],\n",
              " [4501, 2453, 1808, 2101],\n",
              " [651, 2830, 3081, 1571, 304, 1677, 2021, 4466, 419, 88, 161],\n",
              " [2440, 3202, 4878, 1083, 1532, 780, 892, 4058, 3461, 2688, 1356],\n",
              " [4049, 4977, 3309, 2561, 4838, 4546, 2446, 114, 388, 2986, 3593, 3651],\n",
              " [1341, 1096, 1678, 3808, 1515, 735, 3882, 1520, 1161],\n",
              " [2561, 2237, 3689, 516, 81, 942, 4359, 822, 3461, 2688, 1356],\n",
              " [889, 1359, 554, 2992, 3983, 2400, 664, 2627, 4933, 483],\n",
              " [980, 3661, 2668, 2829, 726, 1359, 1588, 3461, 4153, 4933, 4316],\n",
              " [1667, 2561, 3565, 78, 665, 141, 2830, 1173, 3232, 3461, 2688, 1356],\n",
              " [1700, 3937, 1588, 4172, 1509, 1726, 3922, 430, 3898, 3974],\n",
              " [2119, 4319, 1270, 224, 693, 3311, 2914, 485],\n",
              " [2561, 335, 1660, 1329, 57, 3837, 2781, 505, 4510, 4918, 3461, 2688, 1356],\n",
              " [3386, 404, 421, 3374, 2411, 3725, 3629, 1658, 4018],\n",
              " [4845, 742, 2496, 3593, 1956, 1560, 1803, 3063, 3461, 2688, 1356],\n",
              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IL-oHsUvRxxw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "aae9b3a3-3e39-48a8-8e64-284d22eb090d"
      },
      "source": [
        "#embedding representation\n",
        "\n",
        "sent_length=20\n",
        "embedded_docs=pad_sequences(onehot_repr,padding='pre',maxlen=sent_length)\n",
        "print(embedded_docs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[   0    0    0 ... 3596 1078  420]\n",
            " [   0    0    0 ...  914   20 3651]\n",
            " [   0    0    0 ... 2917  352 2489]\n",
            " ...\n",
            " [   0    0    0 ... 3461 2688 1356]\n",
            " [   0    0    0 ...  407 3786  642]\n",
            " [   0    0    0 ...  893 2460 4516]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8GZRn5dTDhm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "9f1aa9dc-f304-4857-cc6b-683e4af51d37"
      },
      "source": [
        "\n",
        "embedded_docs[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   0,    0,    0,    0,    0,    0,    0,    0,    0,    0, 4973,\n",
              "        140, 4170, 2748,  114, 1736, 1279, 3596, 1078,  420], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agg6moN2TFp5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "2845d537-a6d4-4908-e7ee-870b2f8db29a"
      },
      "source": [
        "#creating model\n",
        "embedding_vector_features=40\n",
        "model=Sequential()\n",
        "model.add(Embedding(voc_size,embedding_vector_features,input_length=sent_length))\n",
        "model.add(LSTM(100))\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "print(model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 20, 40)            200000    \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 100)               56400     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 256,501\n",
            "Trainable params: 256,501\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBuUtGjCTpqi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "X_final =np.array(embedded_docs)\n",
        "y_final =np.array(y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-ZyLgiCT4Ta",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_final, y_final, test_size=0.33, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DseEW--pUY3Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cb934e3f-130b-465e-93f2-00dcb76c5a44"
      },
      "source": [
        "y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6035,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wl9RcU6CT63-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "a7263ff1-5261-4933-d13d-b05fed2ae312"
      },
      "source": [
        "#model training\n",
        "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=10,batch_size=64)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "192/192 [==============================] - 7s 36ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 0.6743 - val_accuracy: 0.9084\n",
            "Epoch 2/10\n",
            "192/192 [==============================] - 7s 36ms/step - loss: 0.0046 - accuracy: 0.9989 - val_loss: 0.4493 - val_accuracy: 0.9054\n",
            "Epoch 3/10\n",
            "192/192 [==============================] - 7s 35ms/step - loss: 0.0112 - accuracy: 0.9962 - val_loss: 0.5314 - val_accuracy: 0.9087\n",
            "Epoch 4/10\n",
            "192/192 [==============================] - 7s 35ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.5687 - val_accuracy: 0.9064\n",
            "Epoch 5/10\n",
            "192/192 [==============================] - 7s 36ms/step - loss: 0.0024 - accuracy: 0.9997 - val_loss: 0.7187 - val_accuracy: 0.9074\n",
            "Epoch 6/10\n",
            "192/192 [==============================] - 7s 35ms/step - loss: 3.2882e-04 - accuracy: 1.0000 - val_loss: 0.7823 - val_accuracy: 0.9082\n",
            "Epoch 7/10\n",
            "192/192 [==============================] - 7s 35ms/step - loss: 1.3404e-04 - accuracy: 1.0000 - val_loss: 0.8089 - val_accuracy: 0.9072\n",
            "Epoch 8/10\n",
            "192/192 [==============================] - 7s 35ms/step - loss: 8.5400e-05 - accuracy: 1.0000 - val_loss: 0.8201 - val_accuracy: 0.9069\n",
            "Epoch 9/10\n",
            "192/192 [==============================] - 7s 35ms/step - loss: 6.3810e-05 - accuracy: 1.0000 - val_loss: 0.8335 - val_accuracy: 0.9072\n",
            "Epoch 10/10\n",
            "192/192 [==============================] - 7s 37ms/step - loss: 5.1550e-05 - accuracy: 1.0000 - val_loss: 0.8489 - val_accuracy: 0.9065\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ffa9c45db70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "quP14QPkUE13",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "e214aa43-0b99-475b-878d-18788dea922e"
      },
      "source": [
        "y_pred=model.predict_classes(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-97-66f7fe571d01>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMLnea-5U3iC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9TcKTLyPU5zA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "d6f385b3-7f78-42e8-9168-427185e2f91e"
      },
      "source": [
        "confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3116,  303],\n",
              "       [ 261, 2355]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtUHHXcMU7Zw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "862995e9-07b0-4f4a-dd1e-65f5d5c105aa"
      },
      "source": [
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9065451532725767"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KShXYG1xU8wb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}