Dataset_Preprocessing_and_Traditional_ML_Methods.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Dataset Preprocessing and Traditional ML Methods.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPEFPDL4CvtchYVANAkU0Lu",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Daishinkan002/Sentiment-Analysis-of-Marathi-Tweets/blob/main/Dataset_Preprocessing_and_Traditional_ML_Methods.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries"
      ],
      "metadata": {
        "id": "tC36tAQgwDZJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXQ5K01ps3fm",
        "outputId": "71629f67-0a80-4c9d-a15b-2e0eeafe5ded"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.63.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.21.5)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (0.11.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (1.0.2)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.4.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (3.10.0.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (3.1.0)\n"
          ]
        }
      ],
      "source": [
        "!python -m pip install tqdm pandas numpy seaborn matplotlib scikit-learn nltk"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from tqdm import tqdm\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pickle\n",
        "import string\n",
        "from multiprocessing import Pool\n",
        "import re\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
        "\n",
        "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
        "\n",
        "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
        "\n",
        "rcParams['figure.figsize'] = 12, 8\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)"
      ],
      "metadata": {
        "id": "bTPP5tCCs4Xm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing and analyzing"
      ],
      "metadata": {
        "id": "Xf95Kh0dwOuh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train=pd.read_csv(\"Dataset/tweets-train.csv\",encoding=\"utf-8\")\n",
        "df_test=pd.read_csv(\"Dataset/tweets-test.csv\",encoding=\"utf-8\")\n",
        "df_valid=pd.read_csv(\"Dataset/tweets-valid.csv\",encoding=\"utf-8\")"
      ],
      "metadata": {
        "id": "GPEkWGXMs-1T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head()"
      ],
      "metadata": {
        "id": "2lHFFD1Ptm6h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train[\"label\"].value_counts()"
      ],
      "metadata": {
        "id": "5xFwB5HItxzw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test[\"label\"].value_counts()"
      ],
      "metadata": {
        "id": "uNFpF32Yt0lO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_valid[\"label\"].value_counts()"
      ],
      "metadata": {
        "id": "21LcsRdYt8dF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# extract stop and rare words\n",
        "import string\n",
        "import re\n",
        "import operator\n",
        "def extract_rare_stop(df):\n",
        "  stop_words=[]\n",
        "  rare_words=[]\n",
        "  token_count = {}\n",
        "  nc=0\n",
        "  for line in df['tweet']:\n",
        "    line = line.strip()\n",
        "    line = re.split(r'\\s+|[!$%&()*+, -./:;<=>?\\^_`{|}~]\\s*', line)\n",
        "    line = [token.strip() for token in line if token != '' and token != ' ' and token != '\\n']\n",
        "    if len(line) > 1:\n",
        "      for i in range(len(line)):\n",
        "        if line[i].lower() in token_count:\n",
        "          token_count[line[i].lower()] += 1\n",
        "        else:\n",
        "          token_count[line[i].lower()] = 1\n",
        "      nc=nc+1\n",
        "\n",
        "  token_count = dict(sorted(token_count.items(), key=operator.itemgetter(1)))\n",
        "  for key, value in token_count.items():\n",
        "    if value >= 100:\n",
        "      stop_words.append(key.lower())\n",
        "    elif value <2:\n",
        "      rare_words.append(key.lower())\n",
        "  return rare_words,stop_words"
      ],
      "metadata": {
        "id": "hLjBRHwS4uJC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rare_words, stop_words =  extract_rare_stop(df_train)"
      ],
      "metadata": {
        "id": "CRne_sJm5sys"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(rare_words),len(stop_words))"
      ],
      "metadata": {
        "id": "N5v0CFg355tV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rare_words[:10]"
      ],
      "metadata": {
        "id": "E5Uevx6X6AdG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stop_words[:10]"
      ],
      "metadata": {
        "id": "d7LJZsqJ6bxl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# stop words rare words\n",
        "\n",
        "## removal of stopwords & rarewords changes the meaning of it so to maintain the context we didn't do it"
      ],
      "metadata": {
        "id": "jn5_3Vji7EZ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# strip -> punctuations  -> @ mentions delete -> url delete ->  remove # from #-tokens -> stop words delete (no delete )  -> rare words no delete)\n",
        "def preprocess(line):\n",
        "\n",
        "  line = line.strip()\n",
        "  line = re.sub(r'\\S*\\.com\\S*',\" \",line)\n",
        "  line = re.sub(r'\\S*\\.in\\S*',\" \",line)\n",
        "  line = re.sub(r'\\S*\\.net\\S*',\" \",line)\n",
        "  line = re.sub(r'\\S*\\.me\\S*',\" \",line)\n",
        "  line = re.sub(r'\\s+:\\s+',\" \",line)\n",
        "  line = re.sub(r'\\s+-\\s+',\" \",line)\n",
        "  \n",
        "  \n",
        "  line = re.sub(r'/https?\\/\\/(.*\\.(in))\\//',\" \",line)\n",
        "  line = re.split(r'\\s+|[!$%&()*+./;<=>?\\^`{|}~\\'\\\"]\\s*', line)\n",
        "  line = [token.strip() for token in line if token != '' and token != ' ' and token != '\\n']\n",
        "  \n",
        "  if len(line) > 1:\n",
        "    tokens = []\n",
        "    for i in range(len(line)):\n",
        "      if line[i][0] == '#':\n",
        "        tokens.append(line[i][1:])\n",
        "      elif line[i][0] == '@' or line[i][0] in string.punctuation :\n",
        "        continue\n",
        "      else:\n",
        "        tokens.append(line[i])\n",
        "    return tokens\n"
      ],
      "metadata": {
        "id": "aO-ORuq1wj3a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocess(\"\"\"  󾭴...अन्यथा \"\"सुपारीवाले' म्हणून पाहिले जाईल : खा. राजू शेट्टी\n",
        "21 मे 2016 - सकाळ \n",
        "पुणे - '''शेतकऱ्यांची चळवळ ही... fb.me/1cIUGmX6V\n",
        "\"\"\")"
      ],
      "metadata": {
        "id": "3gJULym51Eye"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with Pool(5) as pool:\n",
        " extracted = pool.map(preprocess, tqdm(df_train['tweet']))\n",
        " print(extracted[:5])"
      ],
      "metadata": {
        "id": "xYdraLfc6uNG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train[\"tuned_tweet\"] = extracted"
      ],
      "metadata": {
        "id": "NCsWEP5f7nMF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with Pool(5) as pool:\n",
        " extracted = pool.map(preprocess, tqdm(df_test['tweet']))\n",
        " print(extracted[:5])"
      ],
      "metadata": {
        "id": "UmtvqDN67bQT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test[\"tuned_tweet\"] = extracted"
      ],
      "metadata": {
        "id": "oOND9k5u7dEQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with Pool(5) as pool:\n",
        " extracted = pool.map(preprocess, tqdm(df_valid['tweet']))\n",
        " print(extracted[:5])"
      ],
      "metadata": {
        "id": "uQZ0vHACCUNQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_valid[\"tuned_tweet\"] = extracted"
      ],
      "metadata": {
        "id": "V7Et1Jx6CVhA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.dropna(inplace=True)\n",
        "df_test.dropna(inplace=True)\n",
        "df_valid.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "iKfY_VkeC7hs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['tuned_tweet'] = df_train['tuned_tweet'].apply(lambda x: \" \".join(x))\n",
        "df_test['tuned_tweet'] = df_test['tuned_tweet'].apply(lambda x: \" \".join(x))\n",
        "df_valid['tuned_tweet'] = df_valid['tuned_tweet'].apply(lambda x: \" \".join(x))"
      ],
      "metadata": {
        "id": "2ME1cbelCoFn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(df_train),len(df_test),len(df_valid))"
      ],
      "metadata": {
        "id": "pjfW5wYZDtjw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_train[\"label\"].value_counts(),df_test[\"label\"].value_counts(),df_valid[\"label\"].value_counts())"
      ],
      "metadata": {
        "id": "g2W78juoEEZi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset Loading"
      ],
      "metadata": {
        "id": "gaDc0mN-QJA_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "id": "nzLM9i3cQYqk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_pickle('/content/gdrive/MyDrive/Major_Project/Marathi-Sentiment/Refined_Dataset/marathi_train.pkl')\n",
        "df_test = pd.read_pickle('/content/gdrive/MyDrive/Major_Project/Marathi-Sentiment/Refined_Dataset/marathi_test.pkl')\n",
        "df_valid = pd.read_pickle('/content/gdrive/MyDrive/Major_Project/Marathi-Sentiment/Refined_Dataset/marathi_valid.pkl')"
      ],
      "metadata": {
        "id": "zUSPe5MAQb6N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Naive Bayes - ML"
      ],
      "metadata": {
        "id": "kBoLa1CXvqP4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n",
        "\n",
        "\n",
        "model.fit(df_train.tuned_tweet, df_train.label)\n",
        "labels = model.predict(df_test.tuned_tweet)"
      ],
      "metadata": {
        "id": "4phKZmbPt-6N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "mat = confusion_matrix(df_test.label, labels)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[-1,0,1], yticklabels=[-1,0,1])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "9a22Tn6VujHa",
        "outputId": "310f17ee-32a3-41ec-9ecc-a24bd6b23e67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(198.86, 0.5, 'predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "image/png": {
              "width": 489,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(df_test.label, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3P1fu3Vdup8C",
        "outputId": "6e1296a0-aead-4410-a9f2-ba6007d2c253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7208888888888889"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_true = df_test.label\n",
        "y_pred = labels\n",
        "from sklearn.metrics import precision_score\n",
        "precision = precision_score(y_true, y_pred,average='macro')\n",
        "from sklearn.metrics import recall_score\n",
        "recall = recall_score(y_true, y_pred,average='macro')\n",
        "print(precision,recall)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8NijS9SmF1Gk",
        "outputId": "ace49b4a-82dd-4ca4-bec0-bfce8d64ca71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7261508030052449 0.720888888888889\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "pr = precision_recall_fscore_support(y_true, y_pred,average='macro')\n",
        "print(pr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDfHDr5xH_qw",
        "outputId": "cc3c5419-3db8-4cc5-8155-c8879032d748"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0.7261508030052449, 0.720888888888889, 0.7183617943047498, None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LinearSvm - ML\n",
        "\n"
      ],
      "metadata": {
        "id": "ECshaTgwviKU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer,CountVectorizer\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "model = make_pipeline(TfidfVectorizer(), LinearSVC())\n",
        "\n",
        "model.fit(df_train.tuned_tweet, df_train.label)\n",
        "labels = model.predict(df_test.tuned_tweet)"
      ],
      "metadata": {
        "id": "13mrG2XjFZJw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "mat = confusion_matrix(df_test.label, labels)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[-1,0,1], yticklabels=[-1,0,1])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "Q2Ls50n6FmGt",
        "outputId": "39e1a5d2-af90-4f2c-ce39-96f5ef23eee6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(198.86, 0.5, 'predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "image/png": {
              "width": 489,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(df_test.label, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDdv3ol1FnuY",
        "outputId": "366c21e4-e73e-4bc8-a434-198bf8e11ef6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7182222222222222"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_true = df_test.label\n",
        "y_pred = labels\n",
        "from sklearn.metrics import precision_score\n",
        "precision = precision_score(y_true, y_pred,average='macro')\n",
        "from sklearn.metrics import recall_score\n",
        "recall = recall_score(y_true, y_pred,average='macro')\n",
        "print(precision,recall)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHs_0moRIm00",
        "outputId": "77ba3eb0-1fee-4931-e50a-4e1dfa604988"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7188489265838488 0.7182222222222222\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "pr = precision_recall_fscore_support(y_true, y_pred,average='macro')\n",
        "print(pr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fODOgXvjIq8V",
        "outputId": "2fb8df48-381c-43e6-9462-bf5451ed3bf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0.7188489265838488, 0.7182222222222222, 0.7180498684465181, None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RBFSvm - ML\n",
        "\n"
      ],
      "metadata": {
        "id": "JfarLA7ftxOF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer,CountVectorizer\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "model = make_pipeline(TfidfVectorizer(), SVC(kernel='rbf', random_state=1, gamma=0.1, C=5))\n",
        "\n",
        "model.fit(df_train.tuned_tweet, df_train.label)\n",
        "labels = model.predict(df_test.tuned_tweet)"
      ],
      "metadata": {
        "id": "WqzQKJZ3txOM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "mat = confusion_matrix(df_test.label, labels)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[-1,0,1], yticklabels=[-1,0,1])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "outputId": "d19ce15c-bda0-414a-d459-e90dbe235edb",
        "id": "4WE-qZFOtxON"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(198.86, 0.5, 'predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "image/png": {
              "width": 489,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(df_test.label, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22b4641e-7deb-42e9-9c03-49b7cd9d5d3a",
        "id": "itG6kuZGtxON"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.728"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_true = df_test.label\n",
        "y_pred = labels\n",
        "from sklearn.metrics import precision_score\n",
        "precision = precision_score(y_true, y_pred,average='macro')\n",
        "from sklearn.metrics import recall_score\n",
        "recall = recall_score(y_true, y_pred,average='macro')\n",
        "print(precision,recall)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "343c4acd-df70-4b42-b782-ff90934fcfd4",
        "id": "6x472QijtxON"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7292481693162299 0.7280000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "pr = precision_recall_fscore_support(y_true, y_pred,average='macro')\n",
        "print(pr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd4df178-b1c9-430b-a7d9-ff04f2637edb",
        "id": "h6ZoPQcLtxON"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0.7292481693162299, 0.7280000000000001, 0.7277935390862743, None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest -ML"
      ],
      "metadata": {
        "id": "v1ezLMTrJmXM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "model = make_pipeline(TfidfVectorizer(), RandomForestClassifier(max_depth=3, random_state=42))\n",
        "\n",
        "\n",
        "model.fit(df_train.tuned_tweet, df_train.label)\n",
        "labels = model.predict(df_test.tuned_tweet)"
      ],
      "metadata": {
        "id": "wCN63KFqJXvL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "mat = confusion_matrix(df_test.label, labels)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=[-1,0,1], yticklabels=[-1,0,1])\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "eRXP_FSrKWii",
        "outputId": "00aa7ab6-8204-4713-bc8b-4ac50de04ee8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(198.86, 0.5, 'predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "image/png": {
              "width": 489,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(df_test.label, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2kL03vAKZOf",
        "outputId": "5f84cd1b-03c4-409c-e910-b49e69d68f6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6537777777777778"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_true = df_test.label\n",
        "y_pred = labels\n",
        "from sklearn.metrics import precision_score\n",
        "precision = precision_score(y_true, y_pred,average='macro')\n",
        "from sklearn.metrics import recall_score\n",
        "recall = recall_score(y_true, y_pred,average='macro')\n",
        "print(precision,recall)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3QtwvzQKbxO",
        "outputId": "a9b7606f-877d-4089-8e8e-133b43d00bb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6672775526790126 0.6537777777777779\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "pr = precision_recall_fscore_support(y_true, y_pred,average='macro')\n",
        "print(pr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-LLwSL3KjvP",
        "outputId": "6c108d72-2dcf-4107-fa08-95e47a07e159"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0.6672775526790126, 0.6537777777777779, 0.6509847772786589, None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ptT4xjUnwMuV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}