News_headlines (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vE4aszyKRg8J"
   },
   "outputs": [],
   "source": [
    "### Get Required Files from Drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "h0TerM9GRl6T",
    "outputId": "776c80b4-6fc0-4b66-ad5a-4ead9ea8b413"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9qPFcop2RsHo"
   },
   "outputs": [],
   "source": [
    "project_path='/content/drive/My Drive/Project/'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8vjq0vSRR5pW"
   },
   "outputs": [],
   "source": [
    "### Read Data \"Sarcasm_Headlines_Dataset.json\". Explore the data and get some insights about the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Etccf8cxR_4V"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json as js"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "4VD23JJXSCaG",
    "outputId": "790590b8-7eec-4d91-eec3-bd70a47bbb7e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['glove.6B.zip',\n",
       " 'glove.6B.zip.1',\n",
       " 'Sarcasm_Headlines_Dataset.json',\n",
       " 'glove.6B.50d.txt',\n",
       " 'glove.6B.100d.txt',\n",
       " 'glove.6B.200d.txt',\n",
       " 'glove.6B.300d.txt']"
      ]
     },
     "execution_count": 95,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "os.chdir(project_path)\n",
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h9IzJ1TISEaO"
   },
   "outputs": [],
   "source": [
    "data=pd.read_json('Sarcasm_Headlines_Dataset.json',lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "0PhybD-eSHOM",
    "outputId": "d32b3d5e-3e7f-4c4a-a224-de98b76ded01"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>article_link</th>\n",
       "      <th>headline</th>\n",
       "      <th>is_sarcastic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/versace-b...</td>\n",
       "      <td>former versace store clerk sues over secret 'b...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/roseanne-...</td>\n",
       "      <td>the 'roseanne' revival catches up to our thorn...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://local.theonion.com/mom-starting-to-fea...</td>\n",
       "      <td>mom starting to fear son's web series closest ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://politics.theonion.com/boehner-just-wan...</td>\n",
       "      <td>boehner just wants wife to listen, not come up...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/jk-rowlin...</td>\n",
       "      <td>j.k. rowling wishes snape happy birthday in th...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        article_link  ... is_sarcastic\n",
       "0  https://www.huffingtonpost.com/entry/versace-b...  ...            0\n",
       "1  https://www.huffingtonpost.com/entry/roseanne-...  ...            0\n",
       "2  https://local.theonion.com/mom-starting-to-fea...  ...            1\n",
       "3  https://politics.theonion.com/boehner-just-wan...  ...            1\n",
       "4  https://www.huffingtonpost.com/entry/jk-rowlin...  ...            0\n",
       "\n",
       "[5 rows x 3 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "TybYGqkcSNBx",
    "outputId": "57beed46-58c9-44f4-97fb-30d60bb1d5ec"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"former versace store clerk sues over secret 'black code' for minority shoppers\",\n",
       "       \"the 'roseanne' revival catches up to our thorny political mood, for better and worse\",\n",
       "       \"mom starting to fear son's web series closest thing she will have to grandchild\",\n",
       "       'boehner just wants wife to listen, not come up with alternative debt-reduction ideas',\n",
       "       'j.k. rowling wishes snape happy birthday in the most magical way'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 98,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences = data[\"headline\"].values\n",
    "sentences[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "uTx5vmeLSUkH",
    "outputId": "eb84004d-a50b-4735-9162-67a4cb654f4f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 0])"
      ]
     },
     "execution_count": 99,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = data[\"is_sarcastic\"].values\n",
    "labels[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "qdtGQxPSSX0K",
    "outputId": "b766ff63-504e-4434-a566-770b220eaf85"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['https://www.huffingtonpost.com/entry/versace-black-code_us_5861fbefe4b0de3a08f600d5',\n",
       "       'https://www.huffingtonpost.com/entry/roseanne-revival-review_us_5ab3a497e4b054d118e04365',\n",
       "       'https://local.theonion.com/mom-starting-to-fear-son-s-web-series-closest-thing-she-1819576697',\n",
       "       'https://politics.theonion.com/boehner-just-wants-wife-to-listen-not-come-up-with-alt-1819574302',\n",
       "       'https://www.huffingtonpost.com/entry/jk-rowling-wishes-snape-happy-birthday_us_569117c4e4b0cad15e64fdcb'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 100,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urls = data[\"article_link\"].values\n",
    "urls[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "id": "9y2V46trSa-0",
    "outputId": "d50e8a2c-5e1e-4631-ce00-7bb2b19c73e7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>headline</th>\n",
       "      <th>is_sarcastic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>former versace store clerk sues over secret 'b...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>the 'roseanne' revival catches up to our thorn...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mom starting to fear son's web series closest ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boehner just wants wife to listen, not come up...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>j.k. rowling wishes snape happy birthday in th...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26704</th>\n",
       "      <td>american politics in moral free-fall</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26705</th>\n",
       "      <td>america's best 20 hikes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26706</th>\n",
       "      <td>reparations and obama</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26707</th>\n",
       "      <td>israeli ban targeting boycott supporters raise...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26708</th>\n",
       "      <td>gourmet gifts for the foodie 2014</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26709 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                headline  is_sarcastic\n",
       "0      former versace store clerk sues over secret 'b...             0\n",
       "1      the 'roseanne' revival catches up to our thorn...             0\n",
       "2      mom starting to fear son's web series closest ...             1\n",
       "3      boehner just wants wife to listen, not come up...             1\n",
       "4      j.k. rowling wishes snape happy birthday in th...             0\n",
       "...                                                  ...           ...\n",
       "26704               american politics in moral free-fall             0\n",
       "26705                            america's best 20 hikes             0\n",
       "26706                              reparations and obama             0\n",
       "26707  israeli ban targeting boycott supporters raise...             0\n",
       "26708                  gourmet gifts for the foodie 2014             0\n",
       "\n",
       "[26709 rows x 2 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Drop article_link from dataset.\n",
    "df=data.drop(['article_link'], axis = 1)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "BqQGWsAQSfAm",
    "outputId": "04c6f5fc-1366-4748-9dd9-b78d2f4c4161"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26709, 2)"
      ]
     },
     "execution_count": 102,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "y18_f7SJSiYj",
    "outputId": "7526e5c2-e412-49ca-962d-786ff986389f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26709 entries, 0 to 26708\n",
      "Data columns (total 2 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   headline      26709 non-null  object\n",
      " 1   is_sarcastic  26709 non-null  int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 417.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "colab_type": "code",
    "id": "fFegeNH6Sky1",
    "outputId": "2a85027a-4668-4a4e-c7d4-34c468b98df6"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>headline</th>\n",
       "      <th>is_sarcastic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26709</td>\n",
       "      <td>26709.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>26602</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>the 20 funniest tweets from women this week</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.438953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.496269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           headline  is_sarcastic\n",
       "count                                         26709  26709.000000\n",
       "unique                                        26602           NaN\n",
       "top     the 20 funniest tweets from women this week           NaN\n",
       "freq                                             10           NaN\n",
       "mean                                            NaN      0.438953\n",
       "std                                             NaN      0.496269\n",
       "min                                             NaN      0.000000\n",
       "25%                                             NaN      0.000000\n",
       "50%                                             NaN      0.000000\n",
       "75%                                             NaN      1.000000\n",
       "max                                             NaN      1.000000"
      ]
     },
     "execution_count": 104,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "gVb8CRFMSuKu",
    "outputId": "cb8cbacf-e23c-4578-a366-6e973c783f90"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of maximum string is : 12\n",
      "Maximum Length: 39\n"
     ]
    }
   ],
   "source": [
    "### Get the Length of each line and find the maximum length\n",
    "df['len'] = df['headline'].apply(lambda x: len(x.split(\" \")))\n",
    "# using len() + key argument + max() \n",
    "res = len(max(df, key = len)) \n",
    "  \n",
    "# printing result \n",
    "print(\"Length of maximum string is : \" + str(res)) \n",
    "df.head()\n",
    "max_length=max([len(headline.split())for headline in df['headline']])\n",
    "print('Maximum Length:',max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "QqWeyASESoHz",
    "outputId": "b7ffe91d-d1bc-484d-a7c0-93ed4519dbd6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking null value:\n",
      " headline        0\n",
      "is_sarcastic    0\n",
      "len             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"checking null value:\\n\",df.isna().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jm3HZFM9Sys6"
   },
   "outputs": [],
   "source": [
    "words = \"./glove.6B.200d.txt\"\n",
    "for item in df.headline:\n",
    "    for inner_item in item.lower().split():\n",
    "        words+=inner_item+\" \"\n",
    "        \n",
    "from wordcloud import WordCloud, STOPWORDS \n",
    "stopwords = set(STOPWORDS) \n",
    "wordcloud = WordCloud(width = 800, height = 800, \n",
    "                background_color ='white', \n",
    "                stopwords = stopwords, \n",
    "                min_font_size = 10).generate(words) \n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Av1MaENpS7sS"
   },
   "outputs": [],
   "source": [
    "### Import required modules required for modelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Oxcy75-DTgoU"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.layers import Dense, Input, LSTM, Embedding, Dropout, Activation, Flatten, Bidirectional, GlobalMaxPool1D\n",
    "from tensorflow.keras.models import Model, Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JGQhNF6xTjbw"
   },
   "outputs": [],
   "source": [
    "max_features = 10000\n",
    "maxlen = 39\n",
    "embedding_size = 200\n",
    "\n",
    "tokenizer = Tokenizer(num_words=max_features)\n",
    "tokenizer.fit_on_texts(list(df['headline']))\n",
    "X = tokenizer.texts_to_sequences(df['headline'])\n",
    "X = pad_sequences(X, maxlen = maxlen)\n",
    "y = df['is_sarcastic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "TWYra9gjTogE",
    "outputId": "c22667c3-4c4f-47be-f0eb-b8129ba13c4b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Samples: 26709\n",
      "[   0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0  307  678 3336 2297   47  381 2575    5 2576 8433]\n",
      "Number of Labels:  26709\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "X = tokenizer.texts_to_sequences(df['headline'])\n",
    "X = pad_sequences(X, maxlen = maxlen)\n",
    "y = np.asarray(df['is_sarcastic'])\n",
    "\n",
    "print(\"Number of Samples:\", len(X))\n",
    "print(X[0])\n",
    "print(\"Number of Labels: \", len(y))\n",
    "print(y[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CQjdP0E-Ttej"
   },
   "outputs": [],
   "source": [
    "glove_file = project_path + \"glove.6B.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wE4uj0BuT2eM"
   },
   "outputs": [],
   "source": [
    "#Extract Glove embedding zip file\n",
    "from zipfile import ZipFile\n",
    "with ZipFile(glove_file, 'r') as z:\n",
    "  z.extractall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kVN8juJrT5q-"
   },
   "outputs": [],
   "source": [
    "EMBEDDING_FILE = './glove.6B.200d.txt'\n",
    "embeddings = {}\n",
    "for o in open(EMBEDDING_FILE):\n",
    "    word = o.split(\" \")[0]\n",
    "    # print(word)\n",
    "    embd = o.split(\" \")[1:]\n",
    "    embd = np.asarray(embd, dtype='float32')\n",
    "    # print(embd)\n",
    "    embeddings[word] = embd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4T1N7bq7T9j0"
   },
   "outputs": [],
   "source": [
    "max_features = 10000\n",
    "maxlen = 39\n",
    "embedding_size = 200\n",
    "\n",
    "tokenizer = Tokenizer(num_words=max_features)\n",
    "tokenizer.fit_on_texts(list(df['headline']))\n",
    "X = tokenizer.texts_to_sequences(df['headline'])\n",
    "X = pad_sequences(X, maxlen = maxlen)\n",
    "y = df['is_sarcastic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DkWZwblsoGCG"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "Py5GQjrDUEZm",
    "outputId": "be907bfa-790c-497c-e77c-e1a397ad2557"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2822: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  if self.run_code(code, result):\n"
     ]
    }
   ],
   "source": [
    "def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')\n",
    "embeddings_index = dict(get_coefs(*o.split(\" \")) for o in open(EMBEDDING_FILE) if len(o)>100)\n",
    "\n",
    "all_embs = np.stack(embeddings_index.values())\n",
    "emb_mean,emb_std = all_embs.mean(), all_embs.std()\n",
    "embed_size = all_embs.shape[1]\n",
    "\n",
    "word_index = tokenizer.word_index\n",
    "nb_words = min(max_features, len(word_index))\n",
    "\n",
    "embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embedding_size))\n",
    "for word, i in word_index.items():\n",
    "    if i >= max_features: continue\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None: embedding_matrix[i] = embedding_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "nu9Wli_GVY1B",
    "outputId": "912f9ec4-4d35-4998-c6ef-1cab38c2a016"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "214/214 [==============================] - 76s 354ms/step - loss: 0.6179 - accuracy: 0.6211 - val_loss: 0.4130 - val_accuracy: 0.8295\n",
      "Epoch 2/5\n",
      "214/214 [==============================] - 76s 357ms/step - loss: 0.3680 - accuracy: 0.8588 - val_loss: 0.3212 - val_accuracy: 0.8615\n",
      "Epoch 3/5\n",
      "214/214 [==============================] - 76s 356ms/step - loss: 0.2477 - accuracy: 0.9137 - val_loss: 0.3551 - val_accuracy: 0.8577\n",
      "Epoch 4/5\n",
      "214/214 [==============================] - 79s 368ms/step - loss: 0.1754 - accuracy: 0.9406 - val_loss: 0.3822 - val_accuracy: 0.8620\n",
      "Epoch 5/5\n",
      "214/214 [==============================] - 80s 372ms/step - loss: 0.1223 - accuracy: 0.9614 - val_loss: 0.4976 - val_accuracy: 0.8602\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(max_features, embedding_size, weights = [embedding_matrix]))\n",
    "model.add(Bidirectional(LSTM(128, return_sequences = True)))\n",
    "model.add(GlobalMaxPool1D())\n",
    "model.add(Dense(40, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(20, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation=\"sigmoid\"))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "batch_size = 100\n",
    "epochs = 5\n",
    "history = model.fit(X, y, batch_size=batch_size, epochs=epochs, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PmtDmoERV5Jv"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ReL9_WuHWefi"
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2g5GF9NFWwD9"
   },
   "outputs": [],
   "source": []
  }
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