Preprocessing_Aassignment_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Preprocessing_Aassignment 1.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNivYNbr5VjcEbZWH9q3tEt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hussain0048/Natural-language-processing/blob/main/Preprocessing_Aassignment_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Setup**"
      ],
      "metadata": {
        "id": "lhS8GMWnNG7J"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "yBJn56riLGMY"
      },
      "outputs": [],
      "source": [
        "import nltk                                # Python library for NLP\n",
        "from nltk.corpus import twitter_samples    # sample Twitter dataset from NLTK\n",
        "import matplotlib.pyplot as plt            # library for visualization\n",
        "import random                              # pseudo-random number generator"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **About the Twitter dataset**"
      ],
      "metadata": {
        "id": "5sHcQK1kNiHd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# downloads sample twitter dataset. uncomment the line below if running on a local machine.\n",
        "nltk.download('twitter_samples')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrcMATj4OxKi",
        "outputId": "fb6267c7-ba90-46f4-964d-b832a14592cf"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package twitter_samples to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/twitter_samples.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# select the set of positive and negative tweets\n",
        "all_positive_tweets = twitter_samples.strings('positive_tweets.json')\n",
        "all_negative_tweets = twitter_samples.strings('negative_tweets.json')"
      ],
      "metadata": {
        "id": "DfRzA6QeZw-O"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Number of positive tweets: ', len(all_positive_tweets))\n",
        "print('Number of negative tweets: ', len(all_negative_tweets))\n",
        "\n",
        "print('\\nThe type of all_positive_tweets is: ', type(all_positive_tweets))\n",
        "print('The type of a tweet entry is: ', type(all_negative_tweets[0]))"
      ],
      "metadata": {
        "id": "UCcT4laAaF4p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Declare a figure with a custom size\n",
        "fig = plt.figure(figsize=(5, 5))\n",
        "\n",
        "# labels for the two classes\n",
        "labels = 'Positives', 'Negative'\n",
        "\n",
        "# Sizes for each slide\n",
        "sizes = [len(all_positive_tweets), len(all_negative_tweets)] \n",
        "\n",
        "# Declare pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
        "plt.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "\n",
        "# Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "plt.axis('equal')  \n",
        "\n",
        "# Display the chart\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "XmkRlnoWa3jm",
        "outputId": "1f01660e-05e1-41e1-fba1-9879c935b9ae"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Looking at raw texts**\n"
      ],
      "metadata": {
        "id": "I1G-gkNmeyBF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print positive in greeen\n",
        "print('\\033[92m' + all_positive_tweets[random.randint(0,5000)])\n",
        "\n",
        "# print negative in red\n",
        "print('\\033[91m' + all_negative_tweets[random.randint(0,5000)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qole2kmoewV6",
        "outputId": "c94f83a4-bac5-4cb3-a049-6ea252190cfd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[92mThanks @TweetsbyHarold he is just amazing. You will have to come and meet him :) @sdchap8\n",
            "\u001b[91m@TheUselessThree @IKyaa_ I wanna duo :(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Preprocess raw text for Sentiment analysis**\n"
      ],
      "metadata": {
        "id": "VvH6IhIGf5pS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Our selected sample. Complex enough to exemplify each step\n",
        "tweet = all_positive_tweets[2277]\n",
        "print(tweet)"
      ],
      "metadata": {
        "id": "riiWOhgyaTzA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " \n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "id": "7ZhSVsoAabsd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re                                  # library for regular expression operations\n",
        "import string                              # for string operations\n",
        "from nltk.corpus import stopwords          # module for stop words that come with NLTK\n",
        "from nltk.stem import PorterStemmer        # module for stemming\n",
        "from nltk.tokenize import TweetTokenizer   # module for tokenizing strings"
      ],
      "metadata": {
        "id": "Mf80YfVhanlN"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Remove hyperlinks, Twitter marks and styles**\n"
      ],
      "metadata": {
        "id": "HiPkxMgpauZ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('\\033[92m' + tweet)\n",
        "print('\\033[94m')\n",
        "\n",
        "# remove old style retweet text \"RT\"\n",
        "tweet2 = re.sub(r'^RT[\\s]+', '', tweet)\n",
        "\n",
        "# remove hyperlinks\n",
        "tweet2 = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', tweet2)\n",
        "\n",
        "# remove hashtags\n",
        "# only removing the hash # sign from the word\n",
        "tweet2 = re.sub(r'#', '', tweet2)\n",
        "\n",
        "print(tweet2)"
      ],
      "metadata": {
        "id": "agfL0Oc2bHzE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Tokenize the string**\n"
      ],
      "metadata": {
        "id": "lRu90Y20bqr8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print()\n",
        "print('\\033[92m' + tweet2)\n",
        "print('\\033[94m')\n",
        "\n",
        "# instantiate tokenizer class\n",
        "tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True,\n",
        "                               reduce_len=True)\n",
        "\n",
        "# tokenize tweets\n",
        "tweet_tokens = tokenizer.tokenize(tweet2)\n",
        "\n",
        "print()\n",
        "print('Tokenized string:')\n",
        "print(tweet_tokens)"
      ],
      "metadata": {
        "id": "kOzp38v-by0L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Remove stop words and punctuations**"
      ],
      "metadata": {
        "id": "Trk9N1_Cd6ns"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import the english stop words list from NLTK\n",
        "stopwords_english = stopwords.words('english') \n",
        "print('Stop words\\n')\n",
        "print(stopwords_english)\n",
        "print('\\nPunctuation\\n')\n",
        "print(string.punctuation)"
      ],
      "metadata": {
        "id": "szJi1yx2etR6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print()\n",
        "print('\\033[92m')\n",
        "print(tweet_tokens)\n",
        "print('\\033[94m')\n",
        "\n",
        "tweets_clean = []\n",
        "\n",
        "for word in tweet_tokens: # Go through every word in your tokens list\n",
        "    if (word not in stopwords_english and  # remove stopwords\n",
        "        word not in string.punctuation):  # remove punctuation\n",
        "        tweets_clean.append(word)\n",
        "\n",
        "print('removed stop words and punctuation:')\n",
        "print(tweets_clean)"
      ],
      "metadata": {
        "id": "7BdY5zs7fIfh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Stemming**"
      ],
      "metadata": {
        "id": "PlnawIOBfb36"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print()\n",
        "print('\\033[92m')\n",
        "print(tweets_clean)\n",
        "print('\\033[94m')\n",
        "\n",
        "# Instantiate stemming class\n",
        "stemmer = PorterStemmer() \n",
        "\n",
        "# Create an empty list to store the stems\n",
        "tweets_stem = [] \n",
        "\n",
        "for word in tweets_clean:\n",
        "    stem_word = stemmer.stem(word)  # stemming word\n",
        "    tweets_stem.append(stem_word)  # append to the list\n",
        "\n",
        "print('stemmed words:')\n",
        "print(tweets_stem)"
      ],
      "metadata": {
        "id": "QjSeagrigHyM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **process_tweet()**"
      ],
      "metadata": {
        "id": "o2IiYkNngVnt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import string\n",
        "import numpy as np\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.tokenize import TweetTokenizer\n",
        "\n",
        "\n",
        "def process_tweet(tweet):\n",
        "    \"\"\"Process tweet function.\n",
        "    Input:\n",
        "        tweet: a string containing a tweet\n",
        "    Output:\n",
        "        tweets_clean: a list of words containing the processed tweet\n",
        "    \"\"\"\n",
        "    stemmer = PorterStemmer()\n",
        "    stopwords_english = stopwords.words('english')\n",
        "    # remove stock market tickers like $GE\n",
        "    tweet = re.sub(r'\\$\\w*', '', tweet)\n",
        "    # remove old style retweet text \"RT\"\n",
        "    tweet = re.sub(r'^RT[\\s]+', '', tweet)\n",
        "    # remove hyperlinks\n",
        "    tweet = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', tweet)\n",
        "    # remove hashtags\n",
        "    # only removing the hash # sign from the word\n",
        "    tweet = re.sub(r'#', '', tweet)\n",
        "    # tokenize tweets\n",
        "    tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True,\n",
        "                               reduce_len=True)\n",
        "    tweet_tokens = tokenizer.tokenize(tweet)\n",
        "\n",
        "    tweets_clean = []\n",
        "    for word in tweet_tokens:\n",
        "        if (word not in stopwords_english and  # remove stopwords\n",
        "                word not in string.punctuation):  # remove punctuation\n",
        "            # tweets_clean.append(word)\n",
        "            stem_word = stemmer.stem(word)  # stemming word\n",
        "            tweets_clean.append(stem_word)\n",
        "\n",
        "    return tweets_clean\n",
        "\n",
        "\n",
        "def build_freqs(tweets, ys):\n",
        "    \"\"\"Build frequencies.\n",
        "    Input:\n",
        "        tweets: a list of tweets\n",
        "        ys: an m x 1 array with the sentiment label of each tweet\n",
        "            (either 0 or 1)\n",
        "    Output:\n",
        "        freqs: a dictionary mapping each (word, sentiment) pair to its\n",
        "        frequency\n",
        "    \"\"\"\n",
        "    # Convert np array to list since zip needs an iterable.\n",
        "    # The squeeze is necessary or the list ends up with one element.\n",
        "    # Also note that this is just a NOP if ys is already a list.\n",
        "    yslist = np.squeeze(ys).tolist()\n",
        "\n",
        "    # Start with an empty dictionary and populate it by looping over all tweets\n",
        "    # and over all processed words in each tweet.\n",
        "    freqs = {}\n",
        "    for y, tweet in zip(yslist, tweets):\n",
        "        for word in process_tweet(tweet):\n",
        "            pair = (word, y)\n",
        "            if pair in freqs:\n",
        "                freqs[pair] += 1\n",
        "            else:\n",
        "                freqs[pair] = 1\n",
        "\n",
        "    return freqs"
      ],
      "metadata": {
        "id": "9Ti8SDkzg6Id"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from utils import process_tweet # Import the process_tweet function\n",
        "\n",
        "# choose the same tweet\n",
        "tweet = all_positive_tweets[2277]\n",
        "\n",
        "print()\n",
        "print('\\033[92m')\n",
        "print(tweet)\n",
        "print('\\033[94m')\n",
        "\n",
        "# call the imported function\n",
        "tweets_stem = process_tweet(tweet); # Preprocess a given tweet\n",
        "\n",
        "print('preprocessed tweet:')\n",
        "print(tweets_stem) # Print the result"
      ],
      "metadata": {
        "id": "919NwH0Hgaue",
        "outputId": "21eb36db-ac7f-4d93-a655-e31de62c3658",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-75e9d71102c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprocess_tweet\u001b[0m \u001b[0;31m# Import the process_tweet function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# choose the same tweet\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtweet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mall_positive_tweets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2277\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'utils'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **References**"
      ],
      "metadata": {
        "id": "wx6pOX7JNll4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[2- Preprocessing -Assignment_Coursesteach](https://coursesteach.com/mod/page/view.php?id=6021&forceview=1)"
      ],
      "metadata": {
        "id": "U1WSBASWNp9c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[1-Preprocessing](https://github.com/ibrahimjelliti/Deeplearning.ai-Natural-Language-Processing-Specialization/blob/master/1%20-%20Natural%20Language%20Processing%20with%20Classification%20and%20Vector%20Spaces/Labs/Week%201/NLP_C1_W1_lecture_nb_01.ipynb)"
      ],
      "metadata": {
        "id": "k4y2pXUiN40q"
      }
    }
  ]
}