Sklearn Tweet Classification (Bahasa).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Project 3 DTS AI - WordCloud.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EjHvP7mOkuLT",
        "colab_type": "text"
      },
      "source": [
        "**MOUNT THE DRIVE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJ29lmjFoYXD",
        "colab_type": "code",
        "outputId": "8c07f70e-3c52-4bc5-d3c4-0365b9664a59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NSNrzLj2lam9",
        "colab_type": "text"
      },
      "source": [
        "**IMPORT LIBRARY AND PACKAGE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ly5wTla4xx7l",
        "colab_type": "code",
        "outputId": "3a5eb6aa-6d70-4177-e66e-1b1698e6f0cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "#Sastrawi is text processing library designed to process text dataset in Bahasa indonesia\n",
        "!pip install sastrawi\n",
        "\n",
        "import json\n",
        "import pandas as pd\n",
        "import re\n",
        "import string\n",
        "import nltk\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
        "from nltk.tokenize import word_tokenize\n",
        "from string import punctuation \n",
        "from nltk.corpus import stopwords\n",
        "from wordcloud import WordCloud\n",
        "from matplotlib import rcParams\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import metrics\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting sastrawi\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6f/4b/bab676953da3103003730b8fcdfadbdd20f333d4add10af949dd5c51e6ed/Sastrawi-1.0.1-py2.py3-none-any.whl (209kB)\n",
            "\u001b[K     |████████████████████████████████| 215kB 4.9MB/s \n",
            "\u001b[?25hInstalling collected packages: sastrawi\n",
            "Successfully installed sastrawi-1.0.1\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pjhj8OTVl9vr",
        "colab_type": "text"
      },
      "source": [
        "**PRE-PROCESSED AND DISPLAY THE DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "II3Orhz9AzyY",
        "colab_type": "code",
        "outputId": "dc01d3aa-9ef6-43a9-c8c3-b895d716cc79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "#variable lst is used to store additional stopwords from file stopword_list_TALA.txt\n",
        "lst = []\n",
        "\n",
        "#variable keysingkat is used to store the value of column singkat in keynorm.csv\n",
        "keysingkat = []\n",
        "\n",
        "#variable keyhasil is used to store the value of column hasil in keynorm.csv\n",
        "keyhasil = []\n",
        "\n",
        "#i am using stopwords from nltk library that stop every unnecessary words in Bahasa indonesia\n",
        "stopwords = nltk.corpus.stopwords.words('indonesian')\n",
        "\n",
        "#For stemming, i am using stemmer factory from Sastrawi\n",
        "factory = StemmerFactory()\n",
        "stemmer = factory.create_stemmer()\n",
        "\n",
        "with open('/content/drive/My Drive/Colab Notebooks/Sentiment/data_latih.json', 'r') as f:\n",
        "    data = json.load(f)\n",
        "df = pd.DataFrame(data)\n",
        "with open(\"/content/drive/My Drive/Colab Notebooks/Sentiment/stopword_list_TALA.txt\", \"r\") as ins:\n",
        "    array = []\n",
        "    for line in ins:\n",
        "        array.append(line)\n",
        "        \n",
        "#Clean the array from new line character /n\n",
        "lst = [array.strip() for array in array]\n",
        "for i in lst:\n",
        "    stopwords.append(i)\n",
        "\n",
        "\n",
        "key = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Sentiment/key_norm.csv', header = 0)\n",
        "for w in key['singkat']:\n",
        "  keysingkat.append(w)\n",
        "for w in key['hasil']:\n",
        "  keyhasil.append(w)\n",
        "  \n",
        "def clean_tweet(tweet):\n",
        "  tweet = tweet.lower()\n",
        "  tweet = re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", tweet)\n",
        "  tweet = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))', 'URL', tweet)\n",
        "  # Replace @handle with the word USER_MENTION\n",
        "  #tweet = re.sub(r'@[\\S]+', 'USER_MENTION', tweet)\n",
        "  tweet = re.sub(r'@[^\\s]+', 'AT_USER', tweet)\n",
        "  # Replaces #hashtag with hashtag\n",
        "  #tweet = re.sub(r'#(\\S+)', r' \\1 ', tweet)\n",
        "  tweet = re.sub(r'#([^\\s]+)', r'\\1', tweet)\n",
        "  # Remove RT (retweet)\n",
        "  tweet = re.sub(r'\\brt\\b', '', tweet)\n",
        "  tweet = re.sub(r'\\buser\\b', '', tweet)\n",
        "  tweet = re.sub(r'\\burl\\b', '', tweet)\n",
        "  tweet = tweet.replace(\"\\n\",\"\") #hilangin \\n atau pake .stripe() /rstripe()\n",
        "  # Replace 2+ dots with space\n",
        "  tweet = re.sub(r'\\.{2,}', ' ', tweet)\n",
        "  # Strip space, \" and ' from tweet\n",
        "  tweet = tweet.strip(' \"\\'')\n",
        "  # Replace multiple spaces with a single space\n",
        "  tweet = re.sub(r'\\s+', ' ', tweet)\n",
        "  \n",
        "  tweet = word_tokenize(tweet)\n",
        "  for i in range(len(tweet)):\n",
        "    for j in range(len(keysingkat)):\n",
        "      if tweet[i] == keysingkat[j]:\n",
        "        tweet[i] = keyhasil[j]\n",
        "  \n",
        "  tweet = [word for word in tweet if word not in stopwords]\n",
        "  \n",
        "  stemmedtweet = []\n",
        "  for w in tweet:\n",
        "    stemmedtweet.append(stemmer.stem(w))\n",
        "  return ' '.join(stemmedtweet)\n",
        "\n",
        "x = df['isi']\n",
        "y = df['sentimen']\n",
        "x = x.apply(clean_tweet)\n",
        "\n",
        "finaltweet = \" \".join(tweet for tweet in x)\n",
        "wordcloud = WordCloud(background_color=\"white\").generate(finaltweet)\n",
        "plt.imshow(wordcloud)\n",
        "plt.axis(\"off\")\n",
        "plt.show()\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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kkv5YLenIFbNwOHVYzL8gkfTH6bRgtxcgliRj0H2CUnUXCCRYzL/gp1zc5t/7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YiiabWsRBHZtRBYXXaCY00N/HZM+Bxh1678nCXn969NBODSvOA82h1xVWR7DMX0zMxet19S1ohG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\nTmL+H0igMuNS0+ymAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OajkEojseYKd",
        "colab_type": "text"
      },
      "source": [
        "**TRANSFORM TEXT INTO NUMERICAL VECTOR USING TDIDF**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8U_uY_A7JI5p",
        "colab_type": "code",
        "outputId": "28cc1c69-e878-40db-eedf-6b8053671795",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#TFIDF stands for Terms Frequency and Inverse-Documet Frequecy, it aims to get the importance score/index of the words in a document. In this project,\n",
        "#iam using TfidfVectorizer from sklearn, this method return a vector in a certain dimension.\n",
        "tfidf = TfidfVectorizer(sublinear_tf=True, ngram_range=(1,3))\n",
        "features = tfidf.fit_transform(x).toarray()\n",
        "labels = y\n",
        "features.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(650, 6969)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6WUOyCMGeioe",
        "colab_type": "text"
      },
      "source": [
        "**TEST THE TRASFORMED TEXT USING MULTINOMIAL NAIVE BAYES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iLhGIf_QCT6S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(x, y, random_state = 0)\n",
        "count_vect = CountVectorizer()\n",
        "X_train_counts = count_vect.fit_transform(X_train)\n",
        "tfidf_transformer = TfidfTransformer()\n",
        "X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
        "\n",
        "clf = MultinomialNB().fit(X_train_tfidf, y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2std9zl1Hg2L",
        "colab_type": "code",
        "outputId": "00fc44e0-8fa6-428b-c068-04782d1169db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(clf.predict(count_vect.transform([\"Ahok pasti juara.\"])))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['positif']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmzVsrXpezdK",
        "colab_type": "text"
      },
      "source": [
        "**CREATE MODEL AND CROSS VALIDATION PARAMETER**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6ytaxZOHn04",
        "colab_type": "code",
        "outputId": "6ff84c06-03d0-4e5d-d4f7-5536c61db891",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Implement model using several algorithm, and doing cross validation at 20 fold. It aims to get the best algorithm for this dataset/case\n",
        "models = [\n",
        "    RandomForestClassifier(n_estimators=200, max_depth=3, random_state=0),\n",
        "    KNeighborsClassifier(n_neighbors=1),\n",
        "    GaussianNB(),\n",
        "    LinearSVC(),\n",
        "    MultinomialNB(),\n",
        "    LogisticRegression(random_state=0),\n",
        "    MLPClassifier()\n",
        "]\n",
        "CV = 20\n",
        "cv_df = pd.DataFrame(index=range(CV * len(models)))\n",
        "entries = []\n",
        "for model in models:\n",
        "  model_name = model.__class__.__name__\n",
        "  accuracies = cross_val_score(model, features, labels, scoring='accuracy', cv=CV)\n",
        "  for fold_idx, accuracy in enumerate(accuracies):\n",
        "    entries.append((model_name, fold_idx, accuracy))\n",
        "cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
            "  \"this warning.\", FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8E-INTDITMK",
        "colab_type": "code",
        "outputId": "aca4afca-26df-4c3a-de8d-632c749e77e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "#Plot the result of the cross validation\n",
        "#sns.boxplot(x='model_name', y='accuracy', data=cv_df)\n",
        "sns.stripplot(x='model_name', y='accuracy', data=cv_df,size=8, jitter=True, edgecolor=\"gray\", linewidth=2)\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CAbxeL1JK7HY7SUlJKtGdAP/617+oqKignSeH7b69ODU7/9t5ArmeTKSU+CJV/Hv7B2yp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\nqj1zWblyJYWFhWHbOh6UsUdCTL1RpojQIyGGTVVe/GYgoQ0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VV9i9ezcKhcfjYdasWdxw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\nxWKx7Jf/B0+nalKcq1gKAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oAwEC3DjL_Ft",
        "colab_type": "code",
        "outputId": "8fc2e25a-8925-4b5b-c579-a3fb27febb3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "cv_df.groupby('model_name').accuracy.mean()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "model_name\n",
              "GaussianNB                0.586703\n",
              "KNeighborsClassifier      0.450458\n",
              "LinearSVC                 0.610250\n",
              "LogisticRegression        0.539633\n",
              "MLPClassifier             0.590124\n",
              "MultinomialNB             0.547452\n",
              "RandomForestClassifier    0.447618\n",
              "Name: accuracy, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dg3CYTMJe-El",
        "colab_type": "text"
      },
      "source": [
        "**DECIDE ON WHAT MODEL WILL BE GOING TO USED**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhbsxJiCMFUU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Implement the classification using LinearSVC Algorithm\n",
        "model = LinearSVC()\n",
        "\n",
        "X_train, X_test, y_train, y_test, indices_train, indices_test = train_test_split(features, labels, df.index, test_size=0.33, random_state=0)\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0y5zGyPPmGi",
        "colab_type": "code",
        "outputId": "f216e4ca-8a71-4ffe-9db7-b33eb850d044",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "conf_mat = confusion_matrix(y_test, y_pred)\n",
        "fig, ax = plt.subplots(figsize=(8,6))\n",
        "sentiment = df['sentimen'].unique()\n",
        "\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(conf_mat, annot=True, fmt='d', xticklabels= sentiment, yticklabels=sentiment)\n",
        "sns.set(rc={'figure.figsize':(8, 6)})\n",
        "ax.set_xlabel('Predicted')\n",
        "ax.set_ylabel('Actual')\n",
        "ax.set_title('Confusion Matrix')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4u922T4cWPgpMAM6KiPsi4vcRMS+weGY+XxzzArB4Z2E66UeSVKuervSTmcOB4Z0cMgj4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\nJEkl/H/RimPifnufhQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Me4c2LxTftlA",
        "colab_type": "text"
      },
      "source": [
        "**LINEAR SVC CLASSIFICATION REPORT**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6eT_EHcPSHZT",
        "colab_type": "code",
        "outputId": "4fd5aa80-14f9-4ef9-cab8-3d00b64a5e37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "print(metrics.classification_report(y_test, y_pred, target_names=df['sentimen'].unique()))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      netral       0.73      0.51      0.60        59\n",
            "     positif       0.67      0.55      0.60        51\n",
            "     negatif       0.63      0.79      0.70       105\n",
            "\n",
            "    accuracy                           0.66       215\n",
            "   macro avg       0.68      0.62      0.63       215\n",
            "weighted avg       0.67      0.66      0.65       215\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NaK3rxmmTW1i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}