Multinomial_NaiveBayes.ipynb

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      "source": [
        "###Sarcasm Detection"
      ],
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        "id": "1KN-eWYM9BTz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Problem Statement:\n",
        "To check for the Sarcasm in a statement using Multinomial Naive Bayes and predicting the model by training the model on a pre-determined database."
      ],
      "metadata": {
        "id": "JSEhRglK9t3_"
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      "cell_type": "markdown",
      "source": [
        "####Imports"
      ],
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "\n",
        "# plotting\n",
        "import seaborn as sns\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "# sklearn\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Dataset:\n",
        "This is the sentiment140 dataset. It contains 1,600,000 tweets extracted using the twitter api . The tweets have been annotated (0 = negative, 4 = positive) and they can be used to detect sentiment .\n",
        "\n",
        "\n"
      ],
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      "source": [
        "df = pd.read_json(\"Sarcasm_Headlines_Dataset_v2.json\", lines = True)\n",
        "df.head()"
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              "   is_sarcastic                                           headline  \\\n",
              "0             1  thirtysomething scientists unveil doomsday clo...   \n",
              "1             0  dem rep. totally nails why congress is falling...   \n",
              "2             0  eat your veggies: 9 deliciously different recipes   \n",
              "3             1  inclement weather prevents liar from getting t...   \n",
              "4             1  mother comes pretty close to using word 'strea...   \n",
              "\n",
              "                                        article_link  \n",
              "0  https://www.theonion.com/thirtysomething-scien...  \n",
              "1  https://www.huffingtonpost.com/entry/donna-edw...  \n",
              "2  https://www.huffingtonpost.com/entry/eat-your-...  \n",
              "3  https://local.theonion.com/inclement-weather-p...  \n",
              "4  https://www.theonion.com/mother-comes-pretty-c...  "
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>eat your veggies: 9 deliciously different recipes</td>\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 28619 entries, 0 to 28618\n",
            "Data columns (total 3 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   is_sarcastic  28619 non-null  int64 \n",
            " 1   headline      28619 non-null  object\n",
            " 2   article_link  28619 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 670.9+ KB\n"
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      "cell_type": "code",
      "source": [
        "#No. of unique categories of tweets\n",
        "df['is_sarcastic'].nunique()     #the dataset contains positive and negative tweets"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "####Pre-Processing:"
      ],
      "metadata": {
        "id": "Ss7A5JEDVxte"
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    {
      "cell_type": "code",
      "source": [
        "data=df[['is_sarcastic','headline']]"
      ],
      "metadata": {
        "id": "d3KtLh6oVz-3"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "#Separating positive and negative tweets\n",
        "data_pos = data[data['is_sarcastic'] == 1]\n",
        "data_neg = data[data['is_sarcastic'] == 0]"
      ],
      "metadata": {
        "id": "QvpCumO4WW-H"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "data_pos = data_pos.iloc[:int(10000)]\n",
        "data_neg = data_neg.iloc[:int(10000)]"
      ],
      "metadata": {
        "id": "udTnChcNWjEj"
      },
      "execution_count": null,
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      "source": [
        "tweets = pd.concat([data_pos, data_neg])\n",
        "tweets"
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              "       is_sarcastic                                           headline\n",
              "0                 1  thirtysomething scientists unveil doomsday clo...\n",
              "3                 1  inclement weather prevents liar from getting t...\n",
              "4                 1  mother comes pretty close to using word 'strea...\n",
              "7                 1  richard branson's global-warming donation near...\n",
              "8                 1  shadow government getting too large to meet in...\n",
              "...             ...                                                ...\n",
              "19154             0  julianne moore on 'freeheld,' marriage equalit...\n",
              "19156             0  report: former nfl kicker threatened students ...\n",
              "19160             0  princess charlotte is 'bonding' quite a lot wi...\n",
              "19162             0  wwii veteran reunites with his long-lost love ...\n",
              "19165             0  the white house's 'week of inclusion' can't un...\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweets.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-EV0x9AYZfP",
        "outputId": "c755b6a2-2010-4370-f946-84fc17bd79f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert text to lowercase\n",
        "tweets['headline'] = tweets['headline'].str.strip().str.lower()\n",
        "text_data=tweets.copy()"
      ],
      "metadata": {
        "id": "17u9IKTsXKsG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Data Visualization:"
      ],
      "metadata": {
        "id": "Vwpum9wvW6pK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.countplot(x='is_sarcastic', data=tweets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "xA15AxrVW3c2",
        "outputId": "82e2bd48-416e-4173-ff33-f69fff09231c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51e1f74390>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEHCAYAAABvHnsJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAR+0lEQVR4nO3de7BdZX3G8e8jEREv3HKGSoKGasYOaq2YAaqjo1Ih2tYwjloclUgZ0wtex7GFTmssSkerrVVbbVOJgHVERClYrUxE1HYqSBDLVUsGRZJyCSSAyngJ/vrHfo/u4jlyeJOzdw77+5nZs9f6rXet9a5MJk/Wu9ZeK1WFJEk9HjLuDkiSFi5DRJLUzRCRJHUzRCRJ3QwRSVK3RePuwKgtXry4li1bNu5uSNKCcfnll99eVVMzLZu4EFm2bBkbN24cdzckacFIcuNsyxzOkiR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEnd5i1EkqxPcluSq4dq+yfZkOT69r1fqyfJ+5NsSnJlksOG1lnd2l+fZPVQ/elJrmrrvD9J5utYJEkzm88zkTOAlfepnQxcVFXLgYvaPMALgOXtswb4EAxCB1gLHAEcDqydDp7W5jVD6913X5KkeTZvIVJVXwG23ae8CjizTZ8JHDtUP6sGLgH2TfIY4BhgQ1Vtq6rtwAZgZVv26Kq6pAYvRDlraFuSpBEZ9S/WD6yqm9v0LcCBbXoJcNNQu82t9svqm2eozyjJGgZnODz2sY/die7D099y1k6trweny999/Li7AMB3T33KuLug3dBj33rVvG17bBfW2xnESF6rWFXrqmpFVa2Ymprx8S+SpA6jDpFb21AU7fu2Vt8CHDzUbmmr/bL60hnqkqQRGnWIXABM32G1Gjh/qH58u0vrSOCuNux1IXB0kv3aBfWjgQvbsruTHNnuyjp+aFuSpBGZt2siST4OPAdYnGQzg7us3gmck+RE4EbgZa3554AXApuAe4ATAKpqW5K3A5e1dqdW1fTF+j9mcAfYw4F/bx9J0gjNW4hU1ctnWXTUDG0LOGmW7awH1s9Q3wg8eWf6KEnaOf5iXZLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSt7GESJI3JbkmydVJPp5krySHJLk0yaYkn0iyZ2v7sDa/qS1fNrSdU1r9W0mOGcexSNIkG3mIJFkCvB5YUVVPBvYAjgPeBby3qp4AbAdObKucCGxv9fe2diQ5tK33JGAl8MEke4zyWCRp0o1rOGsR8PAki4C9gZuB5wHntuVnAse26VVtnrb8qCRp9bOr6kdV9W1gE3D4iPovSWIMIVJVW4D3AN9lEB53AZcDd1bVjtZsM7CkTS8Bbmrr7mjtDxiuz7DO/5NkTZKNSTZu3bp11x6QJE2wcQxn7cfgLOIQ4CDgEQyGo+ZNVa2rqhVVtWJqamo+dyVJE2Ucw1m/BXy7qrZW1U+ATwPPBPZtw1sAS4EtbXoLcDBAW74PcMdwfYZ1JEkjMI4Q+S5wZJK927WNo4BrgYuBl7Q2q4Hz2/QFbZ62/ItVVa1+XLt76xBgOfC1ER2DJInBBe6RqqpLk5wLfB3YAVwBrAM+C5yd5B2tdnpb5XTgo0k2AdsY3JFFVV2T5BwGAbQDOKmq7h3pwUjShBt5iABU1Vpg7X3KNzDD3VVV9UPgpbNs5zTgtF3eQUnSnPiLdUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3cYSIkn2TXJukm8muS7JbybZP8mGJNe37/1a2yR5f5JNSa5MctjQdla39tcnWT2OY5GkSTauM5H3AZ+vql8DngpcB5wMXFRVy4GL2jzAC4Dl7bMG+BBAkv2BtcARwOHA2ungkSSNxshDJMk+wLOB0wGq6sdVdSewCjizNTsTOLZNrwLOqoFLgH2TPAY4BthQVduqajuwAVg5wkORpIk3jjORQ4CtwEeSXJHkw0keARxYVTe3NrcAB7bpJcBNQ+tvbrXZ6pKkERlHiCwCDgM+VFVPA37Az4euAKiqAmpX7TDJmiQbk2zcunXrrtqsJE28cYTIZmBzVV3a5s9lECq3tmEq2vdtbfkW4OCh9Ze22mz1X1BV66pqRVWtmJqa2mUHIkmTbuQhUlW3ADcleWIrHQVcC1wATN9htRo4v01fABzf7tI6ErirDXtdCBydZL92Qf3oVpMkjciiMe33dcDHkuwJ3ACcwCDQzklyInAj8LLW9nPAC4FNwD2tLVW1Lcnbgctau1OratvoDkGSNKcQSXJRVR11f7W5qqpvACtmWPQL22vXR06aZTvrgfU9fZAk7bxfGiJJ9gL2Bha3IaO0RY/GO6EkaeLd35nIHwBvBA4CLufnIXI38Pfz2C9J0gLwS0Okqt4HvC/J66rqAyPqkyRpgZjTNZGq+kCSZwDLhtepqrPmqV+SpAVgrhfWPwo8HvgGcG8rF2CISNIEm+stviuAQ9udUpIkAXP/seHVwK/MZ0ckSQvPXM9EFgPXJvka8KPpYlW9aF56JUlaEOYaIm+bz05Ikhamud6d9eX57ogkaeGZ691Z3+Pnj2bfE3go8IOqevR8dUyStPub65nIo6ank4TB2waPnK9OSZIWhgf8KPj2mtp/ZfB6WknSBJvrcNaLh2YfwuB3Iz+clx5JkhaMud6d9btD0zuA7zAY0pIkTbC5XhM5Yb47IklaeOZ0TSTJ0iTnJbmtfT6VZOl8d06StHub64X1jzB41/lB7fOZVpMkTbC5hshUVX2kqna0zxnA1Dz2S5K0AMw1RO5I8soke7TPK4E75rNjkqTd31xD5PeBlwG3ADcDLwFePU99kiQtEHO9xfdUYHVVbQdIsj/wHgbhIkmaUHM9E/n16QABqKptwNPmp0uSpIViriHykCT7Tc+0M5G5nsVIkh6k5hoEfwN8Nckn2/xLgdPmp0uSpIVirr9YPyvJRuB5rfTiqrp2/rolSVoI5jwk1ULD4JAk/cwDfhS8JEnTDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVK3sYVIexrwFUn+rc0fkuTSJJuSfCLJnq3+sDa/qS1fNrSNU1r9W0mOGc+RSNLkGueZyBuA64bm3wW8t6qeAGwHTmz1E4Htrf7e1o4khwLHAU8CVgIfTLLHiPouSWJMIdJerfvbwIfbfBj8Gv7c1uRM4Ng2varN05Yf1dqvAs6uqh9V1beBTcDhozkCSRKM70zk74A/AX7a5g8A7qyqHW1+M7CkTS8BbgJoy+9q7X9Wn2Gd/yfJmiQbk2zcunXrrjwOSZpoIw+RJL8D3FZVl49qn1W1rqpWVNWKqSnf6itJu8o4Huf+TOBFSV4I7AU8GngfsG+SRe1sYymwpbXfAhwMbE6yCNiHwat5p+vThteRJI3AyM9EquqUqlpaVcsYXBj/YlW9AriYwWt3AVYD57fpC9o8bfkXq6pa/bh299YhwHLgayM6DEkSu9eLpf4UODvJO4ArgNNb/XTgo0k2AdsYBA9VdU2Scxg8WXgHcFJV3Tv6bkvS5BpriFTVl4AvtekbmOHuqqr6IYOXYM20/mn4cixJGht/sS5J6maISJK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRuhogkqZshIknqZohIkroZIpKkboaIJKmbISJJ6maISJK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRuhogkqZshIknqZohIkroZIpKkboaIJKmbISJJ6maISJK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRuhogkqdvIQyTJwUkuTnJtkmuSvKHV90+yIcn17Xu/Vk+S9yfZlOTKJIcNbWt1a399ktWjPhZJmnTjOBPZAby5qg4FjgROSnIocDJwUVUtBy5q8wAvAJa3zxrgQzAIHWAtcARwOLB2OngkSaMx8hCpqpur6utt+nvAdcASYBVwZmt2JnBsm14FnFUDlwD7JnkMcAywoaq2VdV2YAOwcoSHIkkTb6zXRJIsA54GXAocWFU3t0W3AAe26SXATUOrbW612eoz7WdNko1JNm7dunWX9V+SJt3YQiTJI4FPAW+sqruHl1VVAbWr9lVV66pqRVWtmJqa2lWblaSJN5YQSfJQBgHysar6dCvf2oapaN+3tfoW4OCh1Ze22mx1SdKIjOPurACnA9dV1d8OLboAmL7DajVw/lD9+HaX1pHAXW3Y60Lg6CT7tQvqR7eaJGlEFo1hn88EXgVcleQbrfZnwDuBc5KcCNwIvKwt+xzwQmATcA9wAkBVbUvyduCy1u7Uqto2mkOQJMEYQqSq/hPILIuPmqF9ASfNsq31wPpd1ztJ0gPhL9YlSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3RZ8iCRZmeRbSTYlOXnc/ZGkSbKgQyTJHsA/AC8ADgVenuTQ8fZKkibHgg4R4HBgU1XdUFU/Bs4GVo25T5I0MRaNuwM7aQlw09D8ZuCI+zZKsgZY02a/n+RbI+jbJFgM3D7uTuwO8p7V4+6CfpF/P6etzc5u4XGzLVjoITInVbUOWDfufjzYJNlYVSvG3Q9pJv79HI2FPpy1BTh4aH5pq0mSRmChh8hlwPIkhyTZEzgOuGDMfZKkibGgh7OqakeS1wIXAnsA66vqmjF3a5I4RKjdmX8/RyBVNe4+SJIWqIU+nCVJGiNDRJLUzRBRFx83o91VkvVJbkty9bj7MgkMET1gPm5Gu7kzgJXj7sSkMETUw8fNaLdVVV8Bto27H5PCEFGPmR43s2RMfZE0RoaIJKmbIaIePm5GEmCIqI+Pm5EEGCLqUFU7gOnHzVwHnOPjZrS7SPJx4KvAE5NsTnLiuPv0YOZjTyRJ3TwTkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFpFkn+a9x9eKCSvDHJ3kPzn0uy7zj7pAc3fyci7QaSLGo/4tzZ7XwHWFFVt+98r6T755mINIsk32/fj0nylSTfSHJ1kmfN0n6PJGe0NlcleVOrvybJZUn+O8mnps8UWtt/THIp8NdJnpDkC63d15M8Pskjk1zU5q9Ksqqt+4gkn21tr07ye0leDxwEXJzk4tbuO0kWt+njk1zZ1vnovP8BaiJ4JiLNIsn3q+qRSd4M7FVVp7UXcu1dVd+bof3TgXdW1fPb/L5VdWeSA6rqjlZ7B3BrVX0gyRnAYmBVVd3bwuSdVXVekr0Y/Cfvx21/d7cwuARYDrwYWFlVr2nb3aeq7rrvmcj0PHAgcB7wjKq6Pcn+VeU7N7TTPBOR7t9lwAlJ3gY8ZaYAaW4AfjXJB5KsBO5u9Scn+Y8kVwGvAJ40tM4nW4A8ClhSVecBVNUPq+oeIMBfJbkS+AKD97YcCFwFPD/Ju5I8q6ruup9jeF7b1+1t+waIdglDRLof7U15z2bwuPszkhw/S7vtwFOBLwF/CHy4LToDeG1VPQX4S2CvodV+cD+7fwUwBTy9qn4DuJXBWdH/AIcxCJN3JHnrAz8yaecZItL9SPI4BkNQ/8wgGA6bpd1i4CFV9Sngz4faPQq4OclDGYTCL2hnN5uTHNu29bB27WQf4Laq+kmS5wKPa8sPAu6pqn8B3j20r++1/d3XF4GXJjmgrb//A/kzkGazaNwdkBaA5wBvSfIT4PvAjGciDIaaPpJk+j9np7TvvwAuBba275n+kQd4FfBPSU4FfgK8FPgY8Jk2FLYR+GZr+xTg3Ul+2tr+UauvAz6f5H+r6rnTG66qa5KcBnw5yb3AFcCr53b40uy8sC5J6uZwliSpm8NZUod2O+7D7lN+VVVdNY7+SOPicJYkqZvDWZKkboaIJKmbISJJ6maISJK6/R8g9NhyAnAhzgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Multinomial Naive Bayes"
      ],
      "metadata": {
        "id": "HNMgzCRBo68Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Multinomial Naive Bayes algorithm is a Bayesian learning approach popular in Natural Language Processing (NLP). The program guesses the tag of a text, such as an email or a newspaper story, using the Bayes theorem. It calculates each tag's likelihood for a given sample and outputs the tag with the greatest chance."
      ],
      "metadata": {
        "id": "_0R7-QMz4pIs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text_data=text_data.dropna(axis=0,how='any')  #features having missing value/s are removed."
      ],
      "metadata": {
        "id": "wNMuRvYus_xD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split into training and testing data\n",
        "text_x = text_data['headline']\n",
        "text_y = text_data['is_sarcastic']\n",
        "text_x, text_x_test, text_y, text_y_test = train_test_split(text_x,text_y, stratify=text_y, test_size=0.05, random_state=42)"
      ],
      "metadata": {
        "id": "hmZm-LozSzmo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_x.shape"
      ],
      "metadata": {
        "id": "GfrGKUkajQW4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1fb11f48-d3d5-4dd3-dc0f-f74bfb7e9fa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19000,)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_x_test.shape"
      ],
      "metadata": {
        "id": "TCZvDeuqjVDq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06e88e20-695a-4340-8015-45dae8375e5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000,)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_y.shape"
      ],
      "metadata": {
        "id": "HQxkvwLJjYea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55e60f77-83e9-4da4-8278-8fa8b0d7599c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19000,)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_y_test.shape"
      ],
      "metadata": {
        "id": "Z0C79iO0jcx7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5be30ef2-ad3e-4b7e-e0ef-84a32a1b40ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000,)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Vectorization:** To make sense of this data for our machine learning algorithm, we will need to convert each review to a numerical representation that we call vectorization."
      ],
      "metadata": {
        "id": "U-vyLLCm6ZPr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Vectorize tweets to numbers\n",
        "from scipy.sparse import lil_matrix, csr_matrix\n",
        "vec = CountVectorizer(stop_words='english')\n",
        "text_x = vec.fit_transform(text_x)\n",
        "text_x=lil_matrix(text_x).toarray()\n",
        "text_x_test = vec.transform(text_x_test)\n",
        "text_x_test=lil_matrix(text_x_test).toarray()"
      ],
      "metadata": {
        "id": "iBJvi8OWq0IE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = MultinomialNB() #Running the Model\n",
        "model.fit(text_x, text_y)"
      ],
      "metadata": {
        "id": "mGd8gQ1xrdfd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e43b82fc-0ae9-4a55-e321-667a8d7ac207"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_ypred=model.predict(text_x_test) #Predicting Values"
      ],
      "metadata": {
        "id": "K-D9BHp4mM-9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tt = round(model.score(text_x_test,text_y_test),2)*100 #Score\n",
        "print(tt,\"%\")"
      ],
      "metadata": {
        "id": "N1BgIYP3wpwl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96ed584a-1e58-4575-b462-5a2237f94d13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediction on the basis of review."
      ],
      "metadata": {
        "id": "KjS5-jNXwJAJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tweet = input(\"Tweet: \")\n",
        "a = model.predict(vec.transform([tweet]))"
      ],
      "metadata": {
        "id": "vsIuljYFwxk1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "561becd9-af9e-4c04-dcce-9ac480b5aad0"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tweet: I run at nights.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if a == 1:\n",
        "  print(\"Sarcasm\")\n",
        "else:\n",
        "  print(\"Not Sarcasm\")"
      ],
      "metadata": {
        "id": "jw3L0Ka1w760",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9538e31-5c79-4a81-c53c-4462ad511c6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Not Sarcasm\n"
          ]
        }
      ]
    }
  ]
}