sematic_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.6"
    },
    "colab": {
      "name": "nlp-exercise.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPtOP4h64wx7"
      },
      "source": [
        "**Parnian Taghupour**\n",
        "9713010\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4fagGv95BkL"
      },
      "source": [
        "importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "fXi0kRHrErpQ",
        "outputId": "49d4c299-1a9f-4c9c-bd56-c493ce12109f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install contractions"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: contractions in /usr/local/lib/python3.6/dist-packages (0.0.25)\n",
            "Requirement already satisfied: textsearch in /usr/local/lib/python3.6/dist-packages (from contractions) (0.0.17)\n",
            "Requirement already satisfied: pyahocorasick in /usr/local/lib/python3.6/dist-packages (from textsearch->contractions) (1.4.0)\n",
            "Requirement already satisfied: Unidecode in /usr/local/lib/python3.6/dist-packages (from textsearch->contractions) (1.1.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIGpnMrNErpX",
        "outputId": "ac4f5606-fd94-41c3-8258-3f512275ddf6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "import contractions\n",
        "import re\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.feature_selection import chi2,SelectKBest\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "#!pip install nlpaug.augmenter.word as naw "
      ],
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dQfl_f985I2O"
      },
      "source": [
        "this function use a text file to make a dictionary of each formal and informal representation of the word."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIMrhbxqErpb"
      },
      "source": [
        "def lex_norm():\n",
        "    norm_dict = {}\n",
        "    with open(r\"emnlp_dict.txt\") as f:\n",
        "        for line in f:\n",
        "            (key, val) = line.split()\n",
        "            norm_dict[key] = val\n",
        "    return norm_dict"
      ],
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxBLd1b25YQD"
      },
      "source": [
        "use the previous function to formalize words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "who9runWErpj"
      },
      "source": [
        "def formalize(words):\n",
        "    \"\"\"\n",
        "    informal language normalization a list of tokenized words\n",
        "    \"\"\"\n",
        "#     print(\"informal language normalization a list of tokenized words\")\n",
        "    lexical_norm=lex_norm()\n",
        "    formalized_words=[lexical_norm[word] if word in lexical_norm else word for word in words]\n",
        "    \n",
        "    return formalized_words"
      ],
      "execution_count": 213,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_33VxwAeErpv"
      },
      "source": [
        "def replace_contractions(text):\n",
        "    \"\"\"Replace contractions in string of text\"\"\"\n",
        "    return contractions.fix(text)"
      ],
      "execution_count": 214,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qTu09ry6C8N"
      },
      "source": [
        "this function do all the preprosesing:\n",
        "deleting urls \n",
        "\n",
        "*   removing urls\n",
        "*   normalizing\n",
        "*   remove punctuation\n",
        "*   augmenting\n",
        "\n",
        "*   ect.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lF-2Gv-GErp7"
      },
      "source": [
        "# d=replace_contractions(d)\n",
        "                      \n",
        "def preprocess_tweet_text(tweet):\n",
        "    tweet.lower()\n",
        "    # Remove urls\n",
        "    tweet = re.sub(r\"http\\S+|www\\S+|https\\S+\", '', tweet, flags=re.MULTILINE)\n",
        "    # Remove user @ references and '#' from tweet\n",
        "    tweet = re.sub(r'\\@\\w+|\\#','', tweet)\n",
        "    tweet = re.sub(r\"(?:\\@|RT @|https?\\://)\\S+\", \"\", tweet)\n",
        "    tweet=replace_contractions(tweet)\n",
        "    # Remove punctuations\n",
        "    tweet = tweet.translate(str.maketrans('', '', string.punctuation))\n",
        "    # Remove stopwords\n",
        "    tweet_tokens = word_tokenize(tweet)\n",
        "    filtered_words = [w for w in tweet_tokens if not w in stop_words and len(w)>=3]\n",
        "    # filtered_words = [w for w in filtered_words if len(w)>=3]\n",
        "    filtered_words = formalize(filtered_words)\n",
        "    ps = PorterStemmer()\n",
        "    stemmed_words = [ps.stem(w) for w in filtered_words]\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    lemma_words = [lemmatizer.lemmatize(w, pos='a') for w in stemmed_words]\n",
        "    \n",
        "    return \" \".join(filtered_words)"
      ],
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_LPYL6v-7L5h"
      },
      "source": [
        "augmenting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIGIu2JXeCBz"
      },
      "source": [
        "def augment_text(dataframe):\n",
        "    '''\n",
        "    function used to augment minor classes in tweets dataset\n",
        "    '''\n",
        "    augmented_df = pd.DataFrame(columns=['text', 'airline_sentiment'])\n",
        "    augmentor=naw.WordNetAug(aug_p=0.5)\n",
        "    aug_sent={'neutral':3,\n",
        "             'positive':4}\n",
        "    for i in range(len(dataframe)):\n",
        "        current_label=dataframe['airline_sentiment'].iloc[i]\n",
        "        if current_label in list(aug_sent.keys()):\n",
        "            current_text=dataframe['text'].iloc[i]\n",
        "            for j in range(aug_sent[current_label]):\n",
        "                text = augmentor.augment(current_text)\n",
        "                label = current_label\n",
        "                tempdf = pd.DataFrame(list(zip([text], [label])), columns=['text', 'airline_sentiment'])\n",
        "                augmented_df = augmented_df.append(tempdf)\n",
        "        else :\n",
        "                text = dataframe['text'].iloc[i]\n",
        "                label = current_label\n",
        "                tempdf = pd.DataFrame(list(zip([text], [label])), columns=['text', 'airline_sentiment'])\n",
        "                augmented_df = augmented_df.append(tempdf)\n",
        "    return augmented_df\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-06Ij4RE7PGD"
      },
      "source": [
        "opening data files:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTWFvbpL55a-",
        "outputId": "596cc615-2525-41bf-a085-e33d7c1ca4f1",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "import io\n",
        "train_data = pd.read_csv(io.BytesIO(uploaded['airline-train.csv']))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2ee8904b-75d8-4969-a3f2-d7e1d3d810a6\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-2ee8904b-75d8-4969-a3f2-d7e1d3d810a6\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving airline-train.csv to airline-train (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t2VOqepmiDkQ",
        "outputId": "8b054163-0488-4e2c-87ac-0d3f015a37fa",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "import io\n",
        "validation_data = pd.read_csv(io.BytesIO(uploaded['airline-dev.csv']))"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-de3d511b-39c7-44e8-8c99-512b701790ac\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-de3d511b-39c7-44e8-8c99-512b701790ac\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving airline-dev.csv to airline-dev (2).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8whhGlJ8Cfy",
        "outputId": "db5cba41-1433-48f1-d579-2ada414805e4",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "import io\n",
        "test_data = pd.read_csv(io.BytesIO(uploaded['airline-test.csv']))"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-415cb96a-3985-43ff-a050-b5ae6bac7f2a\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-415cb96a-3985-43ff-a050-b5ae6bac7f2a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving airline-test.csv to airline-test (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BhfpCaC7Ue-"
      },
      "source": [
        "changing datas attributes to numbers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Po5NQEm7OXZA"
      },
      "source": [
        "some work to know data better and become familiar with it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5EpuC7t9OMvX",
        "outputId": "7aa5da1e-5226-4c7b-ccf5-c832fc2bcb8b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(\"Shape of the dataframe is\",train_data.shape)\n",
        "print(\"The number of nulls in each column are \\n\", train_data.isna().sum())"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of the dataframe is (8784, 22)\n",
            "The number of nulls in each column are \n",
            " Unnamed: 0                         0\n",
            "_unit_id                           0\n",
            "_golden                            0\n",
            "_unit_state                        0\n",
            "_trusted_judgments                 0\n",
            "_last_judgment_at                 38\n",
            "airline_sentiment                  0\n",
            "airline_sentiment:confidence       0\n",
            "negativereason                  3237\n",
            "negativereason:confidence       2476\n",
            "airline                            0\n",
            "airline_sentiment_gold          8758\n",
            "name                               0\n",
            "negativereason_gold             8761\n",
            "retweet_count                      0\n",
            "text                               0\n",
            "tweet_coord                     8148\n",
            "tweet_created                      0\n",
            "tweet_id                           0\n",
            "tweet_location                  2859\n",
            "user_timezone                   2869\n",
            "target                             0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXgFZJDbErp-",
        "outputId": "8876ba90-b42e-41cc-f4dc-db98a0980c29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        }
      },
      "source": [
        "train_data['target'] = train_data['airline_sentiment'].map({'positive':1,'neutral':0,'negative':-1})\n",
        "validation_data['target'] = validation_data['airline_sentiment'].map({'positive':1,'neutral':0,'negative':-1})\n",
        "test_data['target'] = test_data['airline_sentiment'].map({'positive':1,'neutral':0,'negative':-1})\n",
        "train_data.head()\n",
        "\n"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>_unit_id</th>\n",
              "      <th>_golden</th>\n",
              "      <th>_unit_state</th>\n",
              "      <th>_trusted_judgments</th>\n",
              "      <th>_last_judgment_at</th>\n",
              "      <th>airline_sentiment</th>\n",
              "      <th>airline_sentiment:confidence</th>\n",
              "      <th>negativereason</th>\n",
              "      <th>negativereason:confidence</th>\n",
              "      <th>airline</th>\n",
              "      <th>airline_sentiment_gold</th>\n",
              "      <th>name</th>\n",
              "      <th>negativereason_gold</th>\n",
              "      <th>retweet_count</th>\n",
              "      <th>text</th>\n",
              "      <th>tweet_coord</th>\n",
              "      <th>tweet_created</th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>tweet_location</th>\n",
              "      <th>user_timezone</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11429</td>\n",
              "      <td>681462729</td>\n",
              "      <td>False</td>\n",
              "      <td>finalized</td>\n",
              "      <td>3</td>\n",
              "      <td>2/25/15 3:07</td>\n",
              "      <td>positive</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US Airways</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CaraModisett</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>also appreci hashtag lucycat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2/18/15 10:55</td>\n",
              "      <td>5.681220e+17</td>\n",
              "      <td>Memphis, Tennessee</td>\n",
              "      <td>Central Time (US &amp; Canada)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9717</td>\n",
              "      <td>681461003</td>\n",
              "      <td>False</td>\n",
              "      <td>finalized</td>\n",
              "      <td>3</td>\n",
              "      <td>2/25/15 3:20</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Can't Tell</td>\n",
              "      <td>0.3464</td>\n",
              "      <td>US Airways</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LarrySandeen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>discov bill reschedul flight cost second day l...</td>\n",
              "      <td>[0.0, 0.0]</td>\n",
              "      <td>2/22/15 19:24</td>\n",
              "      <td>5.696990e+17</td>\n",
              "      <td>Southeastern Pennsylvania USA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10153</td>\n",
              "      <td>681461443</td>\n",
              "      <td>False</td>\n",
              "      <td>finalized</td>\n",
              "      <td>3</td>\n",
              "      <td>2/25/15 6:20</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Can't Tell</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>US Airways</td>\n",
              "      <td>NaN</td>\n",
              "      <td>burseka</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>guy suck</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2/22/15 7:28</td>\n",
              "      <td>5.695190e+17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>975</td>\n",
              "      <td>681449647</td>\n",
              "      <td>False</td>\n",
              "      <td>finalized</td>\n",
              "      <td>3</td>\n",
              "      <td>2/25/15 2:14</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Can't Tell</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>United</td>\n",
              "      <td>NaN</td>\n",
              "      <td>artistanxiety</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>right angri</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2/23/15 14:09</td>\n",
              "      <td>5.699820e+17</td>\n",
              "      <td>Punk is the preacher.</td>\n",
              "      <td>Arizona</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1511</td>\n",
              "      <td>681450373</td>\n",
              "      <td>False</td>\n",
              "      <td>finalized</td>\n",
              "      <td>3</td>\n",
              "      <td>2/25/15 6:57</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Can't Tell</td>\n",
              "      <td>0.6848</td>\n",
              "      <td>United</td>\n",
              "      <td>NaN</td>\n",
              "      <td>axelrodaj</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>sure peopl row paid premium seat use overhead ...</td>\n",
              "      <td>[33.94077727, -118.39921036]</td>\n",
              "      <td>2/22/15 18:50</td>\n",
              "      <td>5.696910e+17</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0   _unit_id  ...               user_timezone target\n",
              "0       11429  681462729  ...  Central Time (US & Canada)      1\n",
              "1        9717  681461003  ...                         NaN     -1\n",
              "2       10153  681461443  ...                         NaN     -1\n",
              "3         975  681449647  ...                     Arizona     -1\n",
              "4        1511  681450373  ...  Pacific Time (US & Canada)     -1\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fm-Hwinw8MMZ"
      },
      "source": [
        "applying preprocessing on the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-vgbbGOErqC"
      },
      "source": [
        "train_data['text']=train_data['text'].apply(preprocess)\n",
        "# train_data=augment_text(train_data)  "
      ],
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4rOq4YTjErqF"
      },
      "source": [
        "validation_data['text']=validation_data['text'].apply(preprocess)\n",
        "#validation_data=augment_text(validation_data)  "
      ],
      "execution_count": 218,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eo6HUEVTACEU"
      },
      "source": [
        "test_data['text']=test_data['text'].apply(preprocess)\n",
        "# test_data=augment_text(test_data)  "
      ],
      "execution_count": 219,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4dPQr6oYErqJ",
        "outputId": "0b0ff109-2660-4a59-8c23-0cfa2ce4bc2a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(train_data['text'][:10])"
      ],
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0                         also appreci hashtag lucycat\n",
            "1    discov bill reschedul flight cost second day l...\n",
            "2                                             guy suck\n",
            "3                                          right angri\n",
            "4    sure peopl row paid premium seat use overhead ...\n",
            "5    cancel flight flight way rebook websit applic ...\n",
            "6                                   need help miss bag\n",
            "7    say bag deliv local number say open ask main c...\n",
            "8    start daili ber flight newark frankfurt replac...\n",
            "9        think problem newark saturday due expect snow\n",
            "Name: text, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JD84ZleErqM"
      },
      "source": [
        "x_train=train_data['text']\n",
        "y_train=train_data['target']"
      ],
      "execution_count": 221,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFSekrfljc6U"
      },
      "source": [
        "x_validation = validation_data['text']\n",
        "y_validation = validation_data['target']"
      ],
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60KnmH3VG8XA"
      },
      "source": [
        "**Data vectorization**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T5f4akPYG2e7"
      },
      "source": [
        "Many machine learning models can only be trained on numerical input in the form of vectors or matrices. To prepare our tweets for the machine learning models we create a term frequency-inverse document frequency (tf-idf) vectorization. The result of this vectorization is a sparse matrix which contains a convenient representation of our tweets.\n",
        "\n",
        "The machine learning will learn which word frequency is important to predict a correct sentiment."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x8FfFiHwHLZs"
      },
      "source": [
        "ps:in finding the best parameters, I also give the TfidfVectorizer the paramenter ngram, but the overall result was better without it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZayfXWVhhoVo"
      },
      "source": [
        "tvec = TfidfVectorizer(min_df=7, max_df=0.8, stop_words=stopwords.words('english'))\n",
        "x_train_tfidf = tvec.fit_transform(x_train)\n",
        "x_validation_tfidf = tvec.transform(x_validation)"
      ],
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tErx_zqJHFJw"
      },
      "source": [
        "I use MultinominalNB because the implimentation was incremental means that I could further in the program add data into my model. In order to do so, we should use partial_fit instead of normal fit.\n",
        "I use the function chi 2 from sklearn for feature extracting as the question said, and find out that as the features are more, the result is better."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atPsUjkHErqh",
        "outputId": "5af043ae-28a4-4d2b-adb0-3649042a975d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.feature_selection import SelectKBest, chi2\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "ch2_result = []\n",
        "for n in np.arange(100,1300,100):\n",
        "    ch2 = SelectKBest(chi2, k=n)\n",
        "    x_train_chi2_selected = ch2.fit_transform(x_train_tfidf, y_train)\n",
        "    x_validation_chi2_selected = ch2.transform(x_validation_tfidf)\n",
        "    mnb = MultinomialNB()\n",
        "    mnb.partial_fit(x_train_chi2_selected, y_train, classes=np.unique(y_train))\n",
        "    score = mnb.score(x_validation_chi2_selected, y_validation)\n",
        "    ch2_result.append(score)\n",
        "    print (\"chi2 feature selection evaluation calculated for\"+ str(n) +\"features\"+ str(score))"
      ],
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "chi2 feature selection evaluation calculated for100features0.6775956284153005\n",
            "chi2 feature selection evaluation calculated for200features0.6933060109289617\n",
            "chi2 feature selection evaluation calculated for300features0.703551912568306\n",
            "chi2 feature selection evaluation calculated for400features0.7134562841530054\n",
            "chi2 feature selection evaluation calculated for500features0.7158469945355191\n",
            "chi2 feature selection evaluation calculated for600features0.717896174863388\n",
            "chi2 feature selection evaluation calculated for700features0.7209699453551912\n",
            "chi2 feature selection evaluation calculated for800features0.7219945355191257\n",
            "chi2 feature selection evaluation calculated for900features0.7223360655737705\n",
            "chi2 feature selection evaluation calculated for1000features0.7216530054644809\n",
            "chi2 feature selection evaluation calculated for1100features0.7185792349726776\n",
            "chi2 feature selection evaluation calculated for1200features0.7233606557377049\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ucsXNXWQJgda"
      },
      "source": [
        "train base on best result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cz-sYlONErqn",
        "outputId": "e07af1e7-eb2b-4be5-bd20-8fb1a77cde1d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "ch2 = SelectKBest(chi2, k=1200)\n",
        "x_train_chi2_selected = ch2.fit_transform(x_train_tfidf, y_train)\n",
        "mnb.partial_fit(x_train_chi2_selected, y_train)"
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JUd9G2ovCFIX",
        "outputId": "545a2c24-b256-42ec-dbbe-424228385d21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x_validation_tfidf = tvec.transform(x_validation)\n",
        "x_validation_chi2_selected = ch2.transform(x_validation_tfidf)\n",
        "accuracy = mnb.score(x_validation_chi2_selected, y_validation)\n",
        "print(accuracy)"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7278005464480874\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Fgxtf-iJohX"
      },
      "source": [
        "after cheching huper parameters, I can give my validation data to my model for learning."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWMq9z2ZBuej",
        "outputId": "0e19cae6-ef9c-4099-ac94-cf13b39b11ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "mnb.partial_fit(x_validation_chi2_selected, y_validation)"
      ],
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YvGPEFmLkm9"
      },
      "source": [
        "now working with test data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5j8d1_mK9L7F"
      },
      "source": [
        "x_test = test_data['text']\n",
        "y_test = test_data['target']"
      ],
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jg-aNqGRLsZG"
      },
      "source": [
        "labeling data and finding accuracy then modeling the resulits."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-1j_ZBW8kOn"
      },
      "source": [
        "x_test_tfidf = tvec.transform(x_test)\n",
        "x_test_chi2_selected = ch2.transform(x_test_tfidf)\n",
        "accuracy = mnb.score(x_test_chi2_selected, y_test)\n",
        "print(accuracy)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8aff8pj8MjOd"
      },
      "source": [
        "calculating recall, f1-score, precisios, and confusion matrix to check our results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tin7kw25-Lux",
        "outputId": "29d4f2b4-f731-4c3b-bafa-122348a58bf8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "mnb_predictions = mnb.predict(x_test_chi2_selected)\n",
        "print(classification_report(y_test, mnb_predictions))"
      ],
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.74      0.97      0.84      1826\n",
            "           0       0.69      0.27      0.39       636\n",
            "           1       0.85      0.47      0.61       466\n",
            "\n",
            "    accuracy                           0.74      2928\n",
            "   macro avg       0.76      0.57      0.61      2928\n",
            "weighted avg       0.74      0.74      0.70      2928\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpWtdvQYJfQp",
        "outputId": "0f3561f4-8e05-4e10-dd64-e1303cd36633",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(y_test, mnb_predictions)\n",
        "plt.figure(figsize = (10,5))\n",
        "sn.heatmap(cm, annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hFyVqgrAM0jl"
      },
      "source": [
        "get inputs and then add wath we learnes to our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UqG2_TO0na-w",
        "outputId": "c8e9f287-6770-4200-b7d1-a19fa4f0b67a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "while True:\n",
        "    text = input(\"enter tweets text (or exit to finish): \")\n",
        "    if text == \"exit\":\n",
        "        break\n",
        "    sentiment = input(\"enter tweets sentiment \")\n",
        "    X_test = ch2.transform(tvec.transform(pd.core.series.Series(text)))\n",
        "    y_test = pd.core.series.Series(sentiment)\n",
        "    mnb_predictions = mnb.predict(X_test)\n",
        "    mnb.partial_fit(X_test, y_test)\n",
        "    print( mnb_predictions)"
      ],
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "enter tweets text (or exit to finish): great\n",
            "enter tweets sentiment +1\n",
            "[1]\n",
            "enter tweets text (or exit to finish): bad\n",
            "enter tweets sentiment -1\n",
            "[-1]\n",
            "enter tweets text (or exit to finish): exit\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fi2ALwqcaLRK"
      },
      "source": [
        "# **KNN** **TEST**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1HiyKqnNRyQ"
      },
      "source": [
        "all above steps for knn."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTf2K1plaK3R",
        "outputId": "09144c28-bd83-4d5d-d608-7090a09fc841",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "text_classifier2 = KNeighborsClassifier(n_neighbors = 5)#no of neighbors is hpyer parameter\n",
        "text_classifier2.fit(x_train_chi2_selected, y_train)"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7j1MJdNFaqro"
      },
      "source": [
        "predictions2 = text_classifier2.predict(x_test_chi2_selected)"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ki2BO23qa0C0",
        "outputId": "e287054c-0369-4f5e-8061-424f9836ae91",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "print(confusion_matrix(y_test,predictions2))\n",
        "print(classification_report(y_test,predictions2))\n",
        "print(accuracy_score(y_test, predictions2))"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 689 1106   31]\n",
            " [ 176  410   50]\n",
            " [  85  214  167]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.73      0.38      0.50      1826\n",
            "           0       0.24      0.64      0.35       636\n",
            "           1       0.67      0.36      0.47       466\n",
            "\n",
            "    accuracy                           0.43      2928\n",
            "   macro avg       0.55      0.46      0.44      2928\n",
            "weighted avg       0.61      0.43      0.46      2928\n",
            "\n",
            "0.4323770491803279\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}