Machine_Learning/Text_Preprocessing.ipynb

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    "colab": {
      "name": "Text_Preprocessing.ipynb",
      "provenance": [],
      "collapsed_sections": []
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2B3XfdZsg3_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "728f9a8b-cd33-4511-bf65-554f350ac3e8"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt\n",
        "import nltk\n",
        "import re"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4TLr_6Psg4E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import gensim\n",
        "from gensim.parsing.preprocessing import remove_stopwords\n",
        "from nltk.tokenize import sent_tokenize,word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from gensim.models.doc2vec import Doc2Vec,TaggedDocument\n",
        "from gensim.models import doc2vec"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMDqfhyOskKM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "219ace0e-8edd-463b-cf57-5a49ed1ac260"
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkJ-LsX_slaz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('/content/gdrive/My Drive/Reviews.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FaDaf0eds_7d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "4a04dc3f-03e6-4b2a-9e86-57094ebc4281"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>B001E4KFG0</td>\n",
              "      <td>A3SGXH7AUHU8GW</td>\n",
              "      <td>delmartian</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1303862400</td>\n",
              "      <td>Good Quality Dog Food</td>\n",
              "      <td>I have bought several of the Vitality canned d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>B00813GRG4</td>\n",
              "      <td>A1D87F6ZCVE5NK</td>\n",
              "      <td>dll pa</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1346976000</td>\n",
              "      <td>Not as Advertised</td>\n",
              "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>B000LQOCH0</td>\n",
              "      <td>ABXLMWJIXXAIN</td>\n",
              "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1219017600</td>\n",
              "      <td>\"Delight\" says it all</td>\n",
              "      <td>This is a confection that has been around a fe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>B000UA0QIQ</td>\n",
              "      <td>A395BORC6FGVXV</td>\n",
              "      <td>Karl</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1307923200</td>\n",
              "      <td>Cough Medicine</td>\n",
              "      <td>If you are looking for the secret ingredient i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>B006K2ZZ7K</td>\n",
              "      <td>A1UQRSCLF8GW1T</td>\n",
              "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1350777600</td>\n",
              "      <td>Great taffy</td>\n",
              "      <td>Great taffy at a great price.  There was a wid...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  ...                                               Text\n",
              "0   1  ...  I have bought several of the Vitality canned d...\n",
              "1   2  ...  Product arrived labeled as Jumbo Salted Peanut...\n",
              "2   3  ...  This is a confection that has been around a fe...\n",
              "3   4  ...  If you are looking for the secret ingredient i...\n",
              "4   5  ...  Great taffy at a great price.  There was a wid...\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OATI35W2sg4I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Preprocessor(text):\n",
        "    new_sentence=\"\"\n",
        "    complete_refined_text=\"\"\n",
        "    refined_text = remove_stopwords(text)\n",
        "    refined_text=refined_text.lower()\n",
        "    return refined_text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5iqEdTNsg4L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def lemmatize_text(text):\n",
        "    refined_sentences_list=[]\n",
        "    lemmatizer=WordNetLemmatizer()\n",
        "    sentences=sent_tokenize(text)\n",
        "    for i in range(len(sentences)):\n",
        "        string=\" \"\n",
        "        words=word_tokenize(sentences[i])\n",
        "        words1=[lemmatizer.lemmatize(word) for word in words]\n",
        "        string=string.join(words1)\n",
        "        refined_sentences_list.append(string)\n",
        "    refined_text=\" \"\n",
        "    return (refined_text.join(refined_sentences_list))        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFBIKKjFsg4O",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "def remove_special_chars(text):\n",
        "    text=re.sub(\"([\\(\\[]).*?([\\)\\]])\", \"\\g<1>\\g<2>\", text)\n",
        "    text=re.sub(r'[^A-Za-z0-9]',\" \",text)\n",
        "    return text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arHF7eqUsg4Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pre_processing(text):\n",
        "    text=Preprocessor(text)\n",
        "    text=remove_special_chars(text)\n",
        "    text=lemmatize_text(text)\n",
        "    return text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XNAMWOuItT-m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "5b01d367-63dd-4b31-961c-324b536ad97b"
      },
      "source": [
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')\n"
      ],
      "execution_count": null,
      "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 wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet 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"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEtLmS4wu5Ye",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['processed_text'] = \"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oeFULMQEsg4T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in df.index:\n",
        "  text = df.at[i,'Text']\n",
        "  t1 = pre_processing(text)\n",
        "  df.at[i,'processed_text'] = t1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfL9gPjEtOtH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "ea225bfc-b396-49c4-e9cc-eff9f89a98ac"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "      <th>processed_text</th>\n",
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              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1303862400</td>\n",
              "      <td>Good Quality Dog Food</td>\n",
              "      <td>I have bought several of the Vitality canned d...</td>\n",
              "      <td>i bought vitality canned dog food product good...</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1346976000</td>\n",
              "      <td>Not as Advertised</td>\n",
              "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
              "      <td>product arrived labeled jumbo salted peanut th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>B000LQOCH0</td>\n",
              "      <td>ABXLMWJIXXAIN</td>\n",
              "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1219017600</td>\n",
              "      <td>\"Delight\" says it all</td>\n",
              "      <td>This is a confection that has been around a fe...</td>\n",
              "      <td>this confection century it light pillowy citru...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>B000UA0QIQ</td>\n",
              "      <td>A395BORC6FGVXV</td>\n",
              "      <td>Karl</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1307923200</td>\n",
              "      <td>Cough Medicine</td>\n",
              "      <td>If you are looking for the secret ingredient i...</td>\n",
              "      <td>if looking secret ingredient robitussin i beli...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>B006K2ZZ7K</td>\n",
              "      <td>A1UQRSCLF8GW1T</td>\n",
              "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1350777600</td>\n",
              "      <td>Great taffy</td>\n",
              "      <td>Great taffy at a great price.  There was a wid...</td>\n",
              "      <td>great taffy great price there wide assortment ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  ...                                     processed_text\n",
              "0   1  ...  i bought vitality canned dog food product good...\n",
              "1   2  ...  product arrived labeled jumbo salted peanut th...\n",
              "2   3  ...  this confection century it light pillowy citru...\n",
              "3   4  ...  if looking secret ingredient robitussin i beli...\n",
              "4   5  ...  great taffy great price there wide assortment ...\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cA_sVtHxwKiT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pre_processing1(text):\n",
        "    text=Preprocessor(text)\n",
        "    text=remove_special_chars(text)\n",
        "    return text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iATFTzuLuw4R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "5580f00a-d2d5-4bff-a3a4-bd2e69cc0cdd"
      },
      "source": [
        "#processed text without lemmatization\n",
        "\n",
        "df['unlemm_processed_text'] = \"\"\n",
        "\n",
        "for i in df.index:\n",
        "  text = df.at[i,'Text']\n",
        "  t1 = pre_processing1(text)\n",
        "  df.at[i,'unlemm_processed_text'] = t1\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "      <th>processed_text</th>\n",
              "      <th>unlemm_processed_text</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>B001E4KFG0</td>\n",
              "      <td>A3SGXH7AUHU8GW</td>\n",
              "      <td>delmartian</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1303862400</td>\n",
              "      <td>Good Quality Dog Food</td>\n",
              "      <td>I have bought several of the Vitality canned d...</td>\n",
              "      <td>i bought vitality canned dog food product good...</td>\n",
              "      <td>i bought vitality canned dog food products goo...</td>\n",
              "    </tr>\n",
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              "      <td>B00813GRG4</td>\n",
              "      <td>A1D87F6ZCVE5NK</td>\n",
              "      <td>dll pa</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1346976000</td>\n",
              "      <td>Not as Advertised</td>\n",
              "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
              "      <td>product arrived labeled jumbo salted peanut th...</td>\n",
              "      <td>product arrived labeled jumbo salted peanuts  ...</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>B000LQOCH0</td>\n",
              "      <td>ABXLMWJIXXAIN</td>\n",
              "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1219017600</td>\n",
              "      <td>\"Delight\" says it all</td>\n",
              "      <td>This is a confection that has been around a fe...</td>\n",
              "      <td>this confection century it light pillowy citru...</td>\n",
              "      <td>this confection centuries  it light  pillowy c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>B000UA0QIQ</td>\n",
              "      <td>A395BORC6FGVXV</td>\n",
              "      <td>Karl</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1307923200</td>\n",
              "      <td>Cough Medicine</td>\n",
              "      <td>If you are looking for the secret ingredient i...</td>\n",
              "      <td>if looking secret ingredient robitussin i beli...</td>\n",
              "      <td>if looking secret ingredient robitussin i beli...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>B006K2ZZ7K</td>\n",
              "      <td>A1UQRSCLF8GW1T</td>\n",
              "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1350777600</td>\n",
              "      <td>Great taffy</td>\n",
              "      <td>Great taffy at a great price.  There was a wid...</td>\n",
              "      <td>great taffy great price there wide assortment ...</td>\n",
              "      <td>great taffy great price  there wide assortment...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  ...                              unlemm_processed_text\n",
              "0   1  ...  i bought vitality canned dog food products goo...\n",
              "1   2  ...  product arrived labeled jumbo salted peanuts  ...\n",
              "2   3  ...  this confection centuries  it light  pillowy c...\n",
              "3   4  ...  if looking secret ingredient robitussin i beli...\n",
              "4   5  ...  great taffy great price  there wide assortment...\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4DL_qG0wvaB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(\"final_reviews.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CP8QVWCEw7_h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mv final_reviews.csv '/content/gdrive/My Drive/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMsOff0sx1H1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}