01-NLP-basics/02_Simple_topic_identification.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "TS3VUyUC6aij"
      },
      "source": [
        "---\n",
        "<h1 align='center'><strong>2️⃣Simple topic identification</strong></h1>\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PoSpaoNj6WeF",
        "outputId": "ceb6071c-6f6e-4793-f03a-300ee5f623e4"
      },
      "source": [
        "!git clone https://github.com/mohd-faizy/Natural_Language_Processing_in_Python.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Natural_Language_Processing_in_Python'...\n",
            "remote: Enumerating objects: 152, done.\u001b[K\n",
            "remote: Counting objects: 100% (36/36), done.\u001b[K\n",
            "remote: Compressing objects: 100% (28/28), done.\u001b[K\n",
            "remote: Total 152 (delta 16), reused 27 (delta 8), pack-reused 116\u001b[K\n",
            "Receiving objects: 100% (152/152), 32.91 MiB | 21.54 MiB/s, done.\n",
            "Resolving deltas: 100% (49/49), done.\n"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aw05dwIb9A9U"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "\n",
        "# plt.style.use('fivethirtyeight')\n",
        "# plt.style.use('ggplot')\n",
        "# sns.set_theme()\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "LZeI_v3_aDvw"
      },
      "outputs": [],
      "source": [
        "from pprint import pprint"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "\n",
        "# Download a specific NLTK dataset, e.g., the 'punkt' tokenizer models.\n",
        "nltk.download('punkt', quiet=True)\n",
        "\n",
        "# Download the NLTK stopwords dataset, which contains common stopwords for various languages.\n",
        "nltk.download('stopwords', quiet=True)\n",
        "\n",
        "# Download the WordNet lexical database, which is used for various NLP tasks like synonym and antonym lookup.\n",
        "nltk.download('wordnet', quiet=True)\n",
        "\n",
        "# Download the NLTK averaged perceptron tagger, which is used for part-of-speech tagging.\n",
        "# nltk.download('averaged_perceptron_tagger', quiet=True)\n",
        "\n",
        "# Download the NLTK names dataset, which contains a list of common first names and last names.\n",
        "# nltk.download('names', quit=True)\n",
        "\n",
        "# Download the NLTK movie_reviews dataset, which contains movie reviews categorized as positive and negative.\n",
        "# nltk.download('movie_reviews', quit=True)\n",
        "\n",
        "# Download the NLTK reuters dataset, which is a collection of news documents categorized into topics.\n",
        "# nltk.download('reuters', quit=True)\n",
        "\n",
        "# Download the NLTK brown corpus, which is a collection of text from various genres of written American English.\n",
        "# nltk.download('brown', quit=True)\n",
        "\n",
        "# Download the 'maxent_ne_chunker' dataset, which is used for Named Entity Recognition.\n",
        "# nltk.download('maxent_ne_chunker', quiet=True)\n",
        "\n",
        "# Download the 'words' dataset, which contains a list of common English words.\n",
        "# nltk.download('words', quiet=True)"
      ],
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      "outputs": [
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        "outputId": "72947a41-f4a1-4c5e-f499-35e99da47a5e"
      },
      "source": [
        "os.chdir('/content/Natural_Language_Processing_in_Python/01-NLP-basics/_datasets')\n",
        "cwd = os.getcwd()\n",
        "print('Curent working directory is ', cwd)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Curent working directory is  /content/Natural_Language_Processing_in_Python/01-NLP-basics/_datasets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "9H7VtZ_99qrY",
        "outputId": "ce9ab0c1-eaae-4d88-db63-612a8a0638bf"
      },
      "source": [
        "ls"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " articles.csv            grail.txt            \u001b[0m\u001b[01;34mwikipedia_articles\u001b[0m/\n",
            " english_stopwords.txt   \u001b[01;34mnews_articles\u001b[0m/      'Wikipedia articles.zip'\n",
            " fake_or_real_news.csv  'News articles.zip'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6ooPZFKaDvq"
      },
      "source": [
        "This notebook will demonstrate how to use the `bag-of-words` and `Tf-idf` text representation models from the `NLTK` library, as well as the new `Gensim` library.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TOIEmp5vaDvy"
      },
      "source": [
        "## **Word counts with `bag-of-words`**\n",
        "\n",
        "![bow](https://cdn.analyticsvidhya.com/wp-content/uploads/2023/08/image.png)\n",
        "\n",
        "**Bag of words (BoW)** is a way of **representing text as a collection of words**, **disregarding the order or structure of the words** in the text. It is a simple and flexible approach to text representation, and it has been used in a wide range of natural language processing (NLP) tasks, such as``document classification`, `sentiment analysis`, and `machine translation`.\n",
        "\n",
        "To create a bag of words representation for a text, we first need to create a vocabulary of all the unique words in the text. Then, we count the number of times each word appears in the text. This gives us a vector of word counts, which is the bag of words representation for the text.\n",
        "\n",
        "For example, consider the following text:\n",
        "\n",
        ">**I love to eat pizza. Pizza is my favorite food.**\n",
        "\n",
        "The vocabulary for this text is the set of unique words: `{\"I\", \"love\", \"to\", \"eat\", \"pizza\", \"is\", \"my\", \"favorite\", \"food\"}`. The bag of words representation for this text is the vector `[1, 1, 1, 1, 2, 1, 1, 1, 1]`.\n",
        "\n",
        "This vector tells us that the word pizza appears twice in the text, and all the other words appear once.\n",
        "\n",
        "- **Bag-of-words**\n",
        "    - Basic method for finding topics in a text.\n",
        "    - Need to first create tokens using tokenization and then count up all the tokens.\n",
        "    - The more frequent a word, the more important it might be.\n",
        "    - Can be a great way to determine the significant words in a text."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GPFq8YMSaDvz"
      },
      "source": [
        "### Bag-of-words picker\n",
        "It's time for a quick check on your understanding of bag-of-words."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.tokenize import word_tokenize\n",
        "from collections import Counter # The Counter class is a subclass of dict\n",
        "                                # it designed to count the occurrences of elements in a collection.\n",
        "\n",
        "# Input text\n",
        "text = \"The cat is in the box. The cat box.\"\n",
        "\n",
        "# Tokenize the text\n",
        "tokens = word_tokenize(text)\n",
        "print(\"Tokens:\", tokens)\n",
        "\n",
        "# Count the word frequencies\n",
        "word_freq = Counter(tokens)\n",
        "print(\"Word Frequencies:\", word_freq)\n",
        "\n",
        "# Get the most common words with their counts\n",
        "most_common_words = word_freq.most_common(3)\n",
        "print(\"Most Common Words:\", most_common_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgvLPiNEha1x",
        "outputId": "d6d8a52d-4d07-40d9-9685-90e7b95a132c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokens: ['The', 'cat', 'is', 'in', 'the', 'box', '.', 'The', 'cat', 'box', '.']\n",
            "Word Frequencies: Counter({'The': 2, 'cat': 2, 'box': 2, '.': 2, 'is': 1, 'in': 1, 'the': 1})\n",
            "Most Common Words: [('The', 2), ('cat', 2), ('box', 2)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eF9tSiHcaDv2"
      },
      "source": [
        "### Building a Counter with bag-of-words\n",
        "In this exercise, you'll build your first (in this course) bag-of-words counter using a Wikipedia article, which has been pre-loaded as `article`. Try doing the bag-of-words without looking at the full article text, and guessing what the topic is! If you'd like to peek at the title at the end, we've included it as `article_title`. Note that this article text has had very little preprocessing from the raw Wikipedia database entry."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "2NdEMlE_aDv3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4928d14a-45a4-4c15-e216-b021fa403ace"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(',', 85542),\n",
            " (\"''\", 1278),\n",
            " ('computer', 402),\n",
            " ('software', 375),\n",
            " ('``', 345),\n",
            " ('cite', 210),\n",
            " ('ref', 191),\n",
            " (',computer', 187),\n",
            " (\"'s\", 170),\n",
            " ('system', 153)]\n"
          ]
        }
      ],
      "source": [
        "import re\n",
        "from nltk.tokenize import word_tokenize\n",
        "from collections import Counter\n",
        "\n",
        "path = '/content/Natural_Language_Processing_in_Python/01-NLP-basics/_datasets/articles.csv'\n",
        "\n",
        "with open(path, 'r') as file:\n",
        "    article = file.read()\n",
        "    article_title = word_tokenize(article)[2]\n",
        "\n",
        "# Tokenize the aricle: tokens\n",
        "tokens = word_tokenize(article)\n",
        "\n",
        "# Convert the tokens into lowercase: lower_tokens\n",
        "lower_tokens = [t.lower() for t in tokens]\n",
        "\n",
        "# Create a Counter with the lowercase tokens: bow_simple\n",
        "bow_simple = Counter(lower_tokens)\n",
        "\n",
        "# Print the 10 most common tokens\n",
        "pprint(bow_simple.most_common(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPccEjXKaDv4"
      },
      "source": [
        "## **Simple text preprocessing**\n",
        "\n",
        "Why we Preprocess?\n",
        "\n",
        "- Helps make for better input data\n",
        "    - When performing machine learning or other statistical methods\n",
        "- Examples\n",
        "    - `Tokenization` to create a bag of words.\n",
        "    - Applying `Lowercasing` can lead to the better results.\n",
        "- `Lemmatization` / `Stemming`\n",
        "    - where we Shorten words to their root stems.\n",
        "- Removing `stopwords`, `punctuation`, or `unwanted tokens` which don't carry much meaning."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAHjbWxCaDv4"
      },
      "source": [
        "### Text Preprocessing Steps\n",
        "\n",
        "1. Remove **HTML Tags**\n",
        "2. **Lowercasing**\n",
        "3. **Tokenization**\n",
        "4. Remove **Special Character**s and **Punctuation**\n",
        "5. **Remove Numbers**\n",
        "6. Remove **URLs** and **Email Addresses**\n",
        "7. Handle Contractions\n",
        "8. Spell Checking and Correction\n",
        "9. Remove Duplicate Words\n",
        "10. Remove Short Words\n",
        "11. Remove **Stop Words**\n",
        "12. **Part-of-Speech** (POS) Tagging\n",
        "13. **Named Entity Recognition** (NER)\n",
        "14. **Stemming** (optional)\n",
        "15. **Lemmatization**\n",
        "16. Normalize Whitespace\n",
        "17. Text Length Filtering (optional)\n",
        "18. Custom Cleaning (optional)\n",
        "19. Token Filtering (optional)\n",
        "20. Vectorization\n",
        "21. **Bag-of-Words **(BoW) or TF-IDF\n",
        "22. **Word Embeddings**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4R6ZnIJOaDv5"
      },
      "source": [
        "> **Note:** Before lemmatizing token through NLTK, you must install `wordnet` package"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libraries\n",
        "import nltk\n",
        "from nltk.corpus import stopwords  # Import the NLTK stopwords corpus\n",
        "from nltk.tokenize import word_tokenize  # Import the NLTK word_tokenize function\n",
        "from collections import Counter  # Import the Counter class from the collections module\n",
        "\n",
        "# Download a specific NLTK dataset, e.g., the 'punkt' tokenizer models.\n",
        "nltk.download('punkt', quiet=True)\n",
        "\n",
        "# Download the NLTK stopwords dataset, which contains common stopwords for various languages.\n",
        "nltk.download('stopwords', quiet=True)\n",
        "\n",
        "# Define the input text\n",
        "text = \"\"\"The cat is in the box.\n",
        "          The cat likes the box.\n",
        "          The box is over the cat.\"\"\"\n",
        "\n",
        "# Tokenize the text into words, convert them to lowercase, and filter out non-alphabetic tokens\n",
        "tokens = [words for words in word_tokenize(text.lower()) if words.isalpha()]\n",
        "\n",
        "# Remove stopwords from the list of tokens\n",
        "no_stops = [token for token in tokens if token not in stopwords.words('english')]\n",
        "\n",
        "# Count the frequency of each word in the filtered list of tokens\n",
        "word_counts = Counter(no_stops)\n",
        "\n",
        "# Find the two most common words in the text\n",
        "most_common_words = word_counts.most_common(2)\n",
        "\n",
        "# Print the result, which includes the most common words and their frequencies\n",
        "print(most_common_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bv_kiPFajAGt",
        "outputId": "b0085ef1-bba4-494f-e139-4456fb5d96e2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('cat', 3), ('box', 3)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "G6OGXuSGaDv5",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'i\\nme\\nmy\\nmy'"
            ],
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              "type": "string"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['uses', 'use', 'use', 'use', 'multiple', 'computer', 'file', 'debugging', 'dmy', 'dmy'] \n",
            "\n",
            "['uses', 'multiple', 'computer', 'file', 'debugging', 'dmy', 'dmy', 'reverse', 'mdy', 'tv'] \n",
            "\n",
            "[('computer', 478),\n",
            " ('software', 375),\n",
            " ('system', 228),\n",
            " ('program', 225),\n",
            " ('cite', 210),\n",
            " ('language', 197),\n",
            " ('ref', 191),\n",
            " ('code', 153),\n",
            " ('programming', 139),\n",
            " ('bug', 128)]\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from collections import Counter\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "# Download a specific NLTK dataset, e.g., the 'punkt' tokenizer models.\n",
        "nltk.download('punkt', quiet=True)\n",
        "# Download the NLTK stopwords dataset, which contains common stopwords for various languages.\n",
        "nltk.download('stopwords', quiet=True)\n",
        "# Download the WordNet lexical database, which is used for various NLP tasks like synonym and antonym lookup.\n",
        "nltk.download('wordnet', quiet=True)\n",
        "\n",
        "\n",
        "path = '/content/Natural_Language_Processing_in_Python/01-NLP-basics/_datasets/english_stopwords.txt'\n",
        "\n",
        "with open(path, 'r') as file:\n",
        "    english_stops = file.read()\n",
        "\n",
        "display(english_stops[:10], \"\\n\")\n",
        "\n",
        "# Retain alphabetic words: alpha_only\n",
        "alpha_only = [t for t in lower_tokens if t.isalpha()]\n",
        "print(alpha_only[:10], \"\\n\")\n",
        "\n",
        "# Remove all stop words: no_stops\n",
        "no_stops = [t for t in alpha_only if t not in english_stops]\n",
        "print(no_stops[:10], \"\\n\")\n",
        "\n",
        "# Instantiate the WordNetLemmatizer\n",
        "wordnet_lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "# Lemmatize all tokens into a new list: lemmatized\n",
        "lemmatized = [wordnet_lemmatizer.lemmatize(t) for t in no_stops]\n",
        "\n",
        "# Create the bag-of-words: bow\n",
        "bow = Counter(lemmatized)\n",
        "\n",
        "# Print the 10 most common tokens\n",
        "pprint(bow.most_common(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIPVSejaaDv6"
      },
      "source": [
        "## **Introduction to `GenSim`**\n",
        "\n",
        "- `Gensim` stands for **Generate Similar**.\n",
        "    - **Gensim** is a **Python library** for NLP.\n",
        "    - It is used to represent `documents` and `words` as `vectors`.\n",
        "    - You can use Gensim to do a variety of NLP tasks, such as:\n",
        "        - `Topic modeling`: Gensim can help you identify the main topics ina collection of documents. This can be useful for tasks such asorganizing documents or generating summaries.\n",
        "        - `Machine translation`: Gensim can be used to translate text fromone language to another.\n",
        "        - `Sentiment analysis`: Gensim can be used to determine thesentiment of a piece of text, such as whether it is positive,negative, or neutral.\n",
        "        \n",
        "    - Features provide by `Gensim`\n",
        "        - Provides pre-trained word vector models such as:\n",
        "            - `FastText`\n",
        "            - `Word2Vec`\n",
        "        - Number of Algorithms for **Topic modeling**:\n",
        "            - *Latent Semantic Analysis* (`LSA`)\n",
        "            - *Latent Dirichlet Allocation* (`LDA`)\n",
        "        - `TF-IDF`\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gensim\n",
        "from gensim.corpora.dictionary import Dictionary\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "# Sample corpus\n",
        "corpus = [\n",
        "    'The movie was about a spaceship and aliens.',\n",
        "    'I really liked the movie!',\n",
        "    'Awesome action scenes, but boring characters.',\n",
        "    'The movie was awful! I hate alien films.',\n",
        "    'Space is cool! I liked the movie.',\n",
        "    'More space films, please!.'\n",
        "]\n",
        "\n",
        "# Define and remove stopwords\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "# Tokenize and preprocess, convert to lowercase\n",
        "processed_corpus = []\n",
        "for sentence in corpus:\n",
        "    words = word_tokenize(sentence.lower())  # Tokenize and convert to lowercase in one step\n",
        "    words = [word for word in words if word not in stop_words]  # Remove stopwords\n",
        "    processed_corpus.append(words)\n",
        "\n",
        "# Create a dictionary of words with unique IDs\n",
        "dictionary = Dictionary(processed_corpus)\n",
        "\n",
        "# Print the token-to-ID mapping of words in the dictionary\n",
        "print(dictionary.token2id)\n",
        "\n",
        "# Create a corpus of document representations as bags of words\n",
        "corpus = [dictionary.doc2bow(doc) for doc in processed_corpus]\n",
        "\n",
        "# Print the resulting corpus\n",
        "print(corpus)"
      ],
      "metadata": {
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        },
        "id": "_ZjnvbGVO3oa",
        "outputId": "fc15dcc0-eced-466e-c04c-4cb667bbbc5e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'.': 0, 'aliens': 1, 'movie': 2, 'spaceship': 3, '!': 4, 'liked': 5, 'really': 6, ',': 7, 'action': 8, 'awesome': 9, 'boring': 10, 'characters': 11, 'scenes': 12, 'alien': 13, 'awful': 14, 'films': 15, 'hate': 16, 'cool': 17, 'space': 18, 'please': 19}\n",
            "[[(0, 1), (1, 1), (2, 1), (3, 1)], [(2, 1), (4, 1), (5, 1), (6, 1)], [(0, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1)], [(0, 1), (2, 1), (4, 1), (13, 1), (14, 1), (15, 1), (16, 1)], [(0, 1), (2, 1), (4, 1), (5, 1), (17, 1), (18, 1)], [(0, 1), (4, 1), (7, 1), (15, 1), (18, 1), (19, 1)]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **What are word vectors?**\n",
        "\n",
        "![wrodvec](https://miro.medium.com/v2/resize:fit:2000/1*SYiW1MUZul1NvL1kc1RxwQ.png)\n",
        "\n",
        "- **What are word vectors and how do they help with NLP?**\n",
        "    - `Multi-dimensional Numerical` representation of a word.\n",
        "    - It is a list of `numbers` that capture the meaning of a word in a way that can be **understood by a computer**.\n",
        "    - Word vectors are created using a variety of methods, but the most common method is to use a `deep learnings` `neural network`.\n",
        "    - They give us insight into relationships between words in a corpus.\n",
        "    - These representations capture **`semantic`** and **`syntactic`** relationships between words based on their usage patterns in a given text corpus.\n",
        "\n",
        "Here's how they work and their importance in NLP:\n",
        "\n",
        "- **Word Vector Creation:**\n",
        "\n",
        "    - To create word vectors, algorithms like `Word2Vec`, `GloVe`, `FastText`, or `BERT` are used.\n",
        "    - These algorithms analyze large amounts of text data to learn word embeddings.\n",
        "    - Words with similar meanings or contexts will be represented as vectors that are close together in this vector space, while words with different meanings will be farther apart.\n",
        "\n",
        "- **Semantic and Syntactic Relationships:**\n",
        "\n",
        "    - Word vectors capture both `semantic` (meaning-related) and `syntactic `(grammar-related) relationships between words.\n",
        "    - **For example**, in the word vector space, \"`king`\" might be close to \"`queen`\" because they share a similar `Semantic`.\n",
        "    - Word vectors can also encode `Syntactic` relationships, e.g., verb forms like \"`run`,\" \"`ran`,\" and \"`running`\" positioned close to each other due to verb tense.\n",
        "\n",
        "- **NLP Applications:**\n",
        "\n",
        "    - Word vectors are used as features in various NLP tasks: `sentiment analysis`, `text classification`, `machine translation`, `named entity recognition(NER)`.\n",
        "    - Representing words numerically makes these tasks more manageable for machine learning models.\n",
        "    - Particularly useful for understanding context in language.\n",
        "\n",
        "- **Dimensionality Reduction:**\n",
        "\n",
        "    - Word vectors typically have a fixed dimensionality, e.g., `100`, `300` dimensions, specified during creation.\n",
        "    - This dimensionality reduction enables the compact and computationally efficient representation of words.\n",
        "\n",
        "- **Transfer Learning:**\n",
        "\n",
        "    - `Pre-trained` word embeddings can be fine-tuned for specific NLP tasks.\n",
        "    - Models trained on large text corpora can leverage pre-trained embeddings like Word2Vec or GloVe for transfer learning.\n",
        "\n",
        "- **Improved Performance:**\n",
        "\n",
        "    - Using word vectors often **`enhances NLP model performance`** compared to traditional **`bag-of-words`** representations.\n",
        "    - They capture more nuanced semantic and syntactic information.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Mp2Vp_ZKOvh7"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZWYrwRDBaDv6"
      },
      "source": [
        "### **Creating and querying a corpus with `GenSim`**\n",
        "\n",
        "**Getting Started:**\n",
        "\n",
        "- import messy articles data from **Wikipedia**.\n",
        "- Preprocessed them by:\n",
        "    -  `lowercasing` all words\n",
        "    - `tokenizing` them,\n",
        "    -  and removing `stopwords` and `punctuation`.\n",
        "    - then stored in a list of document tokens called `articles`.\n",
        "    - and then generate the `gensim` **dictionary** and **corpus**.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7mrKeOcaDv7"
      },
      "source": [
        "### **Gensim bag-of-words**\n",
        "\n",
        "Now, we'll use your new **gensim** `corpus` and `dictionary` to see the most common terms per document and across all documents. we your dictionary to look up the terms.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Glob Module:**\n",
        "\n",
        ">it is used for searching files and directories on your computer using wildcard patterns. It's a convenient way to find and work with files that match specific criteria, such as files with certain extensions or names.\n",
        "\n",
        "```python\n",
        "# Find all .txt files in the current directory\n",
        "txt_files = glob.glob(\"*.txt\")\n",
        "\n",
        "# Find all Python script files in a subdirectory named 'scripts'\n",
        "python_scripts = glob.glob(\"scripts/*.py\")\n",
        "```\n",
        "\n",
        "Here's a simple example that prints all the `.txt` files in the directory.\n",
        "\n",
        "```python\n",
        "import glob\n",
        "\n",
        "txt_files = glob.glob(\"*.txt\")\n",
        "\n",
        "for file in txt_files:\n",
        "    print(file)\n",
        "```"
      ],
      "metadata": {
        "id": "UiRYlC7hAW6I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import glob\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from gensim.corpora.dictionary import Dictionary\n",
        "\n",
        "# Define the path to the directory containing the text files\n",
        "path = \"/content/Natural_Language_Processing_in_Python/01-NLP-basics/_datasets/wikipedia_articles/*.txt\"\n",
        "\n",
        "# Get a list of file paths in the directory\n",
        "path_list = glob.glob(path)\n",
        "\n",
        "# Initialize NLTK stopwords\n",
        "stopwords_en = set(stopwords.words(\"english\"))\n",
        "\n",
        "# Function to tokenize text, convert to lowercase, and filter out non-alphabetic words and stopwords\n",
        "def preprocess_text(text):\n",
        "    tokens = word_tokenize(text)\n",
        "    lower_tokens = [t.lower() for t in tokens]\n",
        "    cleaned_tokens = [t for t in lower_tokens if t.isalpha() and t not in stopwords_en]\n",
        "    return cleaned_tokens\n",
        "\n",
        "articles = []\n",
        "for article_path in path_list:\n",
        "    with open(article_path, 'r') as file:\n",
        "        article_text = file.read()\n",
        "\n",
        "\n",
        "    preprocessed_article = preprocess_text(article_text)\n",
        "    articles.append(preprocessed_article)\n",
        "\n",
        "print(\"Total number of wiki articles: \", len(articles))\n",
        "\n",
        "# Create a Dictionary from the articles\n",
        "dictionary = Dictionary(articles)\n",
        "print(dictionary)\n",
        "\n",
        "# Obtain the id for \"aborting\" using dictionary's token2id and get() methods\n",
        "word_id = dictionary.token2id.get(\"aborting\")\n",
        "print(f\"word_id : {word_id} , word : {dictionary.get(word_id)}\")\n",
        "\n",
        "\n",
        "# Use a list comprehension to create a gensim MmCorpus from dictionary\n",
        "corpus = [dictionary.doc2bow(article) for article in articles]\n",
        "\n",
        "# Print the first 10 word ids with their frequency counts from the fifth document\n",
        "word_freq_pairs = corpus[4][:10]\n",
        "print(word_freq_pairs)\n",
        "\n",
        "# Create a list of (word, frequency) pairs\n",
        "word_freq_list = [(dictionary.get(word_id), frequency) for word_id, frequency in word_freq_pairs]\n",
        "print(word_freq_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UEYKYZDgsFKd",
        "outputId": "a07f49f9-4dac-4d48-ff1e-23ffb5d5e6b0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of wiki articles:  12\n",
            "Dictionary<6211 unique tokens: ['aberdeen', 'able', 'aboard', 'absence', 'academic']...>\n",
            "word_id : 1382 , word : aborting\n",
            "[(1, 1), (7, 5), (16, 1), (18, 2), (19, 2), (20, 1), (22, 1), (32, 1), (33, 1), (35, 1)]\n",
            "[('able', 1), ('access', 5), ('active', 1), ('activity', 2), ('actual', 2), ('actually', 1), ('admiral', 1), ('aid', 1), ('aids', 1), ('air', 1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">The comment \"`Use a list comprehension to create a gensim MmCorpus from dictionary`\" means that the code is using a list comprehension to transform the preprocessed text data (in the form of a list of documents) into a format that Gensim, a natural language processing library, can work with. Specifically, it's converting the text data into a Gensim's \"`MmCorpus`\" format, which is a memory-mapped corpus representation suitable for various NLP tasks like topic modeling.\n",
        "\n",
        "> `doc2bow` is a method used to convert a document into the bag-of-words."
      ],
      "metadata": {
        "id": "DGIjHhbb1uKD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### `defaultidict`"
      ],
      "metadata": {
        "id": "7F6qwe8xWx-q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Difference between `Counter` and `defaultdict`**\n",
        "\n",
        "\n",
        "- `Counter` is used for straightforward counting of all elements in an iterable, and it automatically sets missing elements to `0`.\n",
        "\n",
        "- `defaultdict` is used when you need to customize default values for specific keys in a dictionary-like object and handle missing keys without errors.\n",
        " - Having a default value (`0`) for a letter that's not in the word is like having a safety net. It helps you avoid a mistake called a `KeyError`.\n",
        " - It's useful because it means you don't have to check if a letter is in the word before you count it.\n",
        " - You can just count it, and if it's not there, you'll get `0` instead of an error.\n"
      ],
      "metadata": {
        "id": "MpSJJ3L13H9X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "\n",
        "# Example: Counting word frequencies in a text document\n",
        "text = \"This is a simple example of counting word frequencies in NLP. This example is simple.\"\n",
        "word_freq = Counter(text.split())\n",
        "\n",
        "print(word_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fqYZ2hyZDrM",
        "outputId": "330e1772-4c8a-433a-c7d0-d22c954070ae"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({'This': 2, 'is': 2, 'example': 2, 'a': 1, 'simple': 1, 'of': 1, 'counting': 1, 'word': 1, 'frequencies': 1, 'in': 1, 'NLP.': 1, 'simple.': 1})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "# Example: Counting word frequencies in a text document\n",
        "text = \"This is a simple example of counting word frequencies in NLP. This example is simple.\"\n",
        "\n",
        "word_freq = defaultdict(int)  # Default value is 0 for int\n",
        "\n",
        "words = text.split()\n",
        "\n",
        "for word in words:\n",
        "    word_freq[word] += 1\n",
        "\n",
        "print(word_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cby8g4kwZG-L",
        "outputId": "ffd1715b-06d7-4e10-a167-97763a56d4ff"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "defaultdict(<class 'int'>, {'This': 2, 'is': 2, 'a': 1, 'simple': 1, 'example': 2, 'of': 1, 'counting': 1, 'word': 1, 'frequencies': 1, 'in': 1, 'NLP.': 1, 'simple.': 1})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " It's designed to create dictionaries with default values for keys that don't exist. You specify the default value when creating the defaultdict, and this value is returned if you try to access a non-existent key."
      ],
      "metadata": {
        "id": "mYaKKnU8ZXgQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize a string variable\n",
        "word = \"Programming\"\n",
        "\n",
        "# Initialize an empty dictionary 'd' to store character frequencies\n",
        "d = {}\n",
        "\n",
        "# Iterate through each character in the 'word' string\n",
        "for c in word:          # Check if the character 'c' is not in the dictionary 'd'\n",
        "    if c not in d:\n",
        "        d[c] = 1        # If 'c' is not in 'd', add it to the dictionary with a value of 1\n",
        "    else:\n",
        "        d[c] += 1       # If 'c' is already in 'd', increment its value by 1 to count its frequency\n",
        "\n",
        "# Print the final dictionary 'd' containing character frequencies\n",
        "print(d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MRTuCqyzdAVx",
        "outputId": "30c1962f-a0de-4810-d5ba-df69e8b1af6f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'P': 1, 'r': 2, 'o': 1, 'g': 2, 'a': 1, 'm': 2, 'i': 1, 'n': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We can eliminate the need for checking the `if-else` condition when adding `keys` to our dictionary if we have prior knowledge of the default value for any newly introduced `key`. This is precisely where the `defaultdict` comes into play.\n",
        "\n",
        "- With a `defaultdict`, we can predefine a default value type for keys that do not exist in the dictionary. This means that whenever we access a `non-existent` key, the `defaultdict` automatically provides it with the specified **default value**, sparing us from the manual condition checking and value initialization typically seen in regular dictionaries.\n",
        "\n",
        "\n",
        "**why and when to use `defaultdict`?**\n",
        ">it allows us to set a default value type for keys that are not yet present. By doing so, we eliminate the need to explicitly check for the existence of keys and manually initialize their values, making our code more efficient and easier to understand."
      ],
      "metadata": {
        "id": "LbU-xe1jh8p-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "d = defaultdict(int) # any new key introduced in our dict\n",
        "                     # will have the defalut value equal to 0 in case of interger\n",
        "\n",
        "for c in word:\n",
        "    d[c] += 1\n",
        "\n",
        "print(d)\n",
        "print(d['z']) # key that not present will have the defaullt vlaue eqaul to zero"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqqhxqufvR-p",
        "outputId": "d66dc02a-92a6-462f-cc25-8a3046075218"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "defaultdict(<class 'int'>, {'P': 1, 'r': 2, 'o': 1, 'g': 2, 'a': 1, 'm': 2, 'i': 1, 'n': 1})\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42allkiCaDv7"
      },
      "source": [
        "## **`Tf-idf` with gensim**\n",
        "- TF-IDF\n",
        "    - Term Frequency - Inverse Document Frequency\n",
        "    - Allows you to determine the most important words in each document\n",
        "    - Each corpus may have shared words beyond just stop words\n",
        "    - These words should be down-weighted in importance\n",
        "    - Ensures most common words don't show up as key words\n",
        "    - Keeps document specific frequent words wieghted high\n",
        "- Formula\n",
        "\n",
        "$$ w_{i, j} = \\text{tf}_{i, j} * \\log (\\frac{N}{\\text{df}_i}) $$\n",
        "- $ w_{i, j}=$ tf-idf for token $i$ in document $j$\n",
        "- $ \\text{tf}_{i, j} =$ number of occurences of token $i$ in document $j$\n",
        "- $ \\text{df}_{i} =$ number of documents that contain token $i$\n",
        "- $ N =$ total number of documents"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hfuV2PnFaDv7"
      },
      "source": [
        "### **What is tf-idf?**\n",
        "\n",
        "Calculate the `tf-idf` weight for the word `\"computer\"`, which appears **`5` times** in a document containing `100` words. Given a corpus containing `200` documents, with `20` documents mentioning the word `\"computer\"`, `tf-idf` can be calculated by multiplying **Term Frequency** with **Inverse Document Frequency**.\n",
        "\n",
        "- **Term frequency** = Percentage share of the word compared to all tokens in the document.\n",
        "- **Inverse document frequency** = `logarithm` of the total number of documents in a corpora divided by the number of documents containing the term.\n",
        "\n",
        "$$\n",
        "\\text{TF} = \\frac{5}{100} = 0.05\n",
        "$$\n",
        "\n",
        "$$\n",
        "\\text{IDF} = \\log\\left(\\frac{200}{20}\\right) = \\log(10) = 1\n",
        "$$\n",
        "\n",
        "$$\n",
        "\\text{TF-IDF} = \\text{TF} \\cdot \\text{IDF} = 0.05 \\cdot 1 = 0.05\n",
        "$$\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **TF-idf using Gensim**"
      ],
      "metadata": {
        "id": "ko5HmRwgB36O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import nltk\n",
        "import pandas as pd\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from gensim.models.tfidfmodel import TfidfModel\n",
        "from gensim.corpora import Dictionary\n",
        "from pprint import pprint\n",
        "\n",
        "text = \"\"\"\n",
        "       Natural Language Processing (NLP) is a field of artificial intelligence that focuses\n",
        "       on the interaction between computers and humans through natural language. The ultimate\n",
        "       goal of NLP is to enable computers to understand, interpret, and generate human language\n",
        "       in a way that is both meaningful and useful. NLP techniques are used in a wide range of\n",
        "       applications, including machine translation, speech recognition, sentiment analysis,\n",
        "       chatbots, and information retrieval. It involves various tasks such as tokenization,\n",
        "       part-of-speech tagging, named entity recognition, and syntactic parsing. NLTK is a popular\n",
        "       Python library for NLP, providing tools and resources for tasks like text processing, text\n",
        "       classification, and language modeling. It offers a wide range of functions and datasets\n",
        "       to help you get started with NLP projects. In this sample text, we'll demonstrate some\n",
        "       basic NLP tasks using NLTK, such as tokenization and part-of-speech tagging.\n",
        "       Let's get started!\n",
        "      \"\"\"\n",
        "\n",
        "# Tokenize the text\n",
        "sentences = nltk.sent_tokenize(text)\n",
        "\n",
        "# Tokenize and preprocess the sentences\n",
        "corpus = []\n",
        "wordnet = WordNetLemmatizer()\n",
        "\n",
        "for i in range(len(sentences)):\n",
        "    cleaned_sentence = re.sub('[^a-zA-Z]', ' ', sentences[i])\n",
        "    cleaned_sentence = cleaned_sentence.lower()\n",
        "    cleaned_sentence = cleaned_sentence.split()\n",
        "    cleaned_sentence = [wordnet.lemmatize(word) for word in cleaned_sentence if not word in set(stopwords.words('english'))]\n",
        "    corpus.append(cleaned_sentence)\n",
        "\n",
        "# Create a Gensim Dictionary\n",
        "dictionary = Dictionary(corpus)\n",
        "\n",
        "# Store the BoW representation in a variable\n",
        "bow_corpus = [dictionary.doc2bow(sentence) for sentence in corpus]\n",
        "\n",
        "# Create a Gensim TF-IDF model\n",
        "tfidf = TfidfModel(bow_corpus)\n",
        "\n",
        "# Transform the corpus to TF-IDF representation using the stored BoW\n",
        "tfidf_corpus = tfidf[bow_corpus]\n",
        "\n",
        "# Extract feature names (words) from the dictionary\n",
        "feature_names = [dictionary[id] for id in range(len(dictionary))]\n",
        "\n",
        "# Create a DataFrame to display the TF-IDF matrix\n",
        "tfidf_matrix = []\n",
        "\n",
        "for doc in tfidf_corpus:\n",
        "    tfidf_values = {dictionary[id]: freq for id, freq in doc}\n",
        "    row = [tfidf_values.get(word, 0.0) for word in feature_names]\n",
        "    tfidf_matrix.append(row)\n",
        "\n",
        "# Displaying the TF-IDF matrix using a DataFrame\n",
        "tfidf_df = pd.DataFrame(tfidf_matrix, columns=feature_names)\n",
        "display(tfidf_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "_t-rYfZo7Mu4",
        "outputId": "386722d9-c3c0-4142-fcaf-42ac62f9db96"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   artificial  computer     field     focus     human  intelligence  \\\n",
              "0    0.298243  0.198829  0.298243  0.298243  0.198829      0.298243   \n",
              "1    0.000000  0.209456  0.000000  0.000000  0.209456      0.000000   \n",
              "2    0.000000  0.000000  0.000000  0.000000  0.000000      0.000000   \n",
              "3    0.000000  0.000000  0.000000  0.000000  0.000000      0.000000   \n",
              "4    0.000000  0.000000  0.000000  0.000000  0.000000      0.000000   \n",
              "5    0.000000  0.000000  0.000000  0.000000  0.000000      0.000000   \n",
              "6    0.000000  0.000000  0.000000  0.000000  0.000000      0.000000   \n",
              "7    0.000000  0.000000  0.000000  0.000000  0.000000      0.000000   \n",
              "\n",
              "   interaction  language   natural       nlp  ...       get      help  \\\n",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Your TF-IDF matrix stored in tfidf_df\n",
        "# Make sure tfidf_df is already defined as in your code\n",
        "\n",
        "# Transpose the TF-IDF matrix for the desired format\n",
        "tfidf_df = tfidf_df.transpose()\n",
        "\n",
        "# Plotting the heatmap\n",
        "plt.figure(figsize=(20, 12))\n",
        "sns.heatmap(tfidf_df, cmap='coolwarm', annot=True, fmt=\".2f\", cbar=False, linewidths=0.5)\n",
        "plt.title('TF-IDF Heatmap')\n",
        "plt.xlabel('Documents')\n",
        "plt.ylabel('Terms (Words)')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 909
        },
        "id": "R2yrvz0z7mvH",
        "outputId": "8b36fed6-2951-4193-f193-02a19447c04b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **`TfidfModel` from `Gensim` vs `TfidfVectorizer` from `sklearn`**\n",
        "\n",
        "\n",
        "**Input Data:**\n",
        "\n",
        "- **Gensim's** `TfidfModel`: Takes a bag-of-words corpus as input.\n",
        "- **sklearn's** `TfidfVectorizer`: Takes raw documents as input.\n",
        "\n",
        "**Normalization:**\n",
        "\n",
        "- Both use raw counts as term frequency (TF) and apply `L2` normalization.\n",
        "- sklearn's TfidfVectorizer allows custom normalization ('`L1`', '`L2`', or 'none').\n",
        "\n",
        "**Workflow:**\n",
        "\n",
        "- **Gensim's** `TfidfModel`: Requires creating a dictionary, converting to bag-of-words, and applying the model.\n",
        "- **sklearn's** `TfidfVectorizer`: Combines these steps in a single model (CountVectorizer + TfidfTransformer).\n",
        "\n",
        "**Output:**\n",
        "\n",
        "- **Gensim's** `TfidfModel`:: Outputs a gensim-format corpus (list of lists with word indices and TF-IDF values).\n",
        "- **sklearn's** `TfidfVectorizer`: Outputs a sparse matrix with TF-IDF values for words in documents.\n",
        "\n",
        "**Summary:**\n",
        "\n",
        "- Both libraries compute TF-IDF, differing in workflows, input, and output formats.\n",
        "- Choose based on specific needs and preferences.\n",
        "\n"
      ],
      "metadata": {
        "id": "ZHnm2PGP_7gK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **TF-idf using Sklearn**"
      ],
      "metadata": {
        "id": "3D0k3XGRB_Nt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "f5brY4cLLRQL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af2e93fa-13cf-4ca0-b53f-c53a42b3dc5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['natural language processing nlp field artificial intelligence focus interaction computer human natural language', 'ultimate goal nlp enable computer understand interpret generate human language way meaningful useful', 'nlp technique used wide range application including machine translation speech recognition sentiment analysis chatbots information retrieval', 'involves various task tokenization part speech tagging named entity recognition syntactic parsing', 'nltk popular python library nlp providing tool resource task like text processing text classification language modeling', 'offer wide range function datasets help get started nlp project', 'sample text demonstrate basic nlp task using nltk tokenization part speech tagging', 'let get started']\n",
            "\n",
            "\n",
            "Feature names (words) from the vectorizer: ['analysis' 'application' 'artificial' 'basic' 'chatbots' 'classification'\n",
            " 'computer' 'datasets' 'demonstrate' 'enable' 'entity' 'field' 'focus'\n",
            " 'function' 'generate' 'get' 'goal' 'help' 'human' 'including'\n",
            " 'information' 'intelligence' 'interaction' 'interpret' 'involves'\n",
            " 'language' 'let' 'library' 'like' 'machine' 'meaningful' 'modeling'\n",
            " 'named' 'natural' 'nlp' 'nltk' 'offer' 'parsing' 'part' 'popular'\n",
            " 'processing' 'project' 'providing' 'python' 'range' 'recognition'\n",
            " 'resource' 'retrieval' 'sample' 'sentiment' 'speech' 'started'\n",
            " 'syntactic' 'tagging' 'task' 'technique' 'text' 'tokenization' 'tool'\n",
            " 'translation' 'ultimate' 'understand' 'used' 'useful' 'using' 'various'\n",
            " 'way' 'wide'] \n",
            "\n",
            "   analysis  application  artificial     basic  chatbots  classification  \\\n",
            "0  0.000000     0.000000    0.272682  0.000000  0.000000        0.000000   \n",
            "1  0.000000     0.000000    0.000000  0.000000  0.000000        0.000000   \n",
            "2  0.268416     0.268416    0.000000  0.000000  0.268416        0.000000   \n",
            "3  0.000000     0.000000    0.000000  0.000000  0.000000        0.000000   \n",
            "4  0.000000     0.000000    0.000000  0.000000  0.000000        0.262523   \n",
            "5  0.000000     0.000000    0.000000  0.000000  0.000000        0.000000   \n",
            "6  0.000000     0.000000    0.000000  0.336954  0.000000        0.000000   \n",
            "7  0.000000     0.000000    0.000000  0.000000  0.000000        0.000000   \n",
            "\n",
            "   computer  datasets  demonstrate    enable  ...      tool  translation  \\\n",
            "0  0.228529  0.000000     0.000000  0.000000  ...  0.000000     0.000000   \n",
            "1  0.250676  0.000000     0.000000  0.299108  ...  0.000000     0.000000   \n",
            "2  0.000000  0.000000     0.000000  0.000000  ...  0.000000     0.268416   \n",
            "3  0.000000  0.000000     0.000000  0.000000  ...  0.000000     0.000000   \n",
            "4  0.000000  0.000000     0.000000  0.000000  ...  0.262523     0.000000   \n",
            "5  0.000000  0.352252     0.000000  0.000000  ...  0.000000     0.000000   \n",
            "6  0.000000  0.000000     0.336954  0.000000  ...  0.000000     0.000000   \n",
            "7  0.000000  0.000000     0.000000  0.000000  ...  0.000000     0.000000   \n",
            "\n",
            "   ultimate  understand      used    useful     using   various       way  \\\n",
            "0  0.000000    0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
            "1  0.299108    0.299108  0.000000  0.299108  0.000000  0.000000  0.299108   \n",
            "2  0.000000    0.000000  0.268416  0.000000  0.000000  0.000000  0.000000   \n",
            "3  0.000000    0.000000  0.000000  0.000000  0.000000  0.318537  0.000000   \n",
            "4  0.000000    0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
            "5  0.000000    0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
            "6  0.000000    0.000000  0.000000  0.000000  0.336954  0.000000  0.000000   \n",
            "7  0.000000    0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
            "\n",
            "       wide  \n",
            "0  0.000000  \n",
            "1  0.000000  \n",
            "2  0.224953  \n",
            "3  0.000000  \n",
            "4  0.000000  \n",
            "5  0.295215  \n",
            "6  0.000000  \n",
            "7  0.000000  \n",
            "\n",
            "[8 rows x 68 columns]\n"
          ]
        }
      ],
      "source": [
        "# Cleaning the texts\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import nltk\n",
        "\n",
        "text = \"\"\"\n",
        "       Natural Language Processing (NLP) is a field of artificial intelligence that focuses\n",
        "       on the interaction between computers and humans through natural language. The ultimate\n",
        "       goal of NLP is to enable computers to understand, interpret, and generate human language\n",
        "       in a way that is both meaningful and useful. NLP techniques are used in a wide range of\n",
        "       applications, including machine translation, speech recognition, sentiment analysis,\n",
        "       chatbots, and information retrieval. It involves various tasks such as tokenization,\n",
        "       part-of-speech tagging, named entity recognition, and syntactic parsing. NLTK is a popular\n",
        "       Python library for NLP, providing tools and resources for tasks like text processing, text\n",
        "       classification, and language modeling. It offers a wide range of functions and datasets\n",
        "       to help you get started with NLP projects. In this sample text, we'll demonstrate some\n",
        "       basic NLP tasks using NLTK, such as tokenization and part-of-speech tagging.\n",
        "       Let's get started!\n",
        "      \"\"\"\n",
        "\n",
        "ps = PorterStemmer()\n",
        "wordnet = WordNetLemmatizer()\n",
        "sentences = nltk.sent_tokenize(text)\n",
        "\n",
        "corpus = []\n",
        "\n",
        "for i in range(len(sentences)):\n",
        "    cleaned_sentence = re.sub('[^a-zA-Z]', ' ', sentences[i]) # Removes all characters from sentences[i] that are not alphabetic characters and replaces them with spaces.\n",
        "    cleaned_sentence = cleaned_sentence.lower()\n",
        "    cleaned_sentence = cleaned_sentence.split()\n",
        "    cleaned_sentence = [wordnet.lemmatize(word) for word in cleaned_sentence if not word in set(stopwords.words('english'))]\n",
        "    cleaned_sentence = ' '.join(cleaned_sentence)\n",
        "    corpus.append(cleaned_sentence)\n",
        "\n",
        "print(corpus)\n",
        "print(\"\\n\")\n",
        "\n",
        "# Creating the TF-IDF model\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf_vectorizer = TfidfVectorizer()\n",
        "tfidf_df = tfidf_vectorizer.fit_transform(corpus).toarray()\n",
        "\n",
        "# Get the feature names (words) from the vectorizer\n",
        "feature_names = tfidf_vectorizer.get_feature_names_out()\n",
        "print(\"Feature names (words) from the vectorizer:\", feature_names, \"\\n\")\n",
        "\n",
        "# Printing the TF-IDF matrix\n",
        "import pandas as pd\n",
        "\n",
        "# Creating a DataFrame to display the TF-IDF matrix\n",
        "tfidf_df = pd.DataFrame(tfidf_df, columns=tfidf_vectorizer.get_feature_names_out())\n",
        "\n",
        "# Displaying the TF-IDF matrix\n",
        "print(tfidf_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**heatmap**"
      ],
      "metadata": {
        "id": "_LDHH0lh35H_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Your TF-IDF matrix stored in tfidf_df\n",
        "# Make sure tfidf_df is already defined as in your code\n",
        "\n",
        "# Plotting the heatmap\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.heatmap(tfidf_df, cmap='coolwarm')\n",
        "plt.title('TF-IDF Heatmap')\n",
        "plt.xlabel('Terms (Words)')\n",
        "plt.ylabel('Documents')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "iHUSTp_x2Z3w",
        "outputId": "6ad57f7b-8e0f-49d4-b296-69c184483f57"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "#### $\\color{skyblue}{\\textbf{Connect with me:}}$\n",
        "\n",
        "\n",
        "[<img align=\"left\" src=\"https://cdn4.iconfinder.com/data/icons/social-media-icons-the-circle-set/48/twitter_circle-512.png\" width=\"32px\"/>][twitter]\n",
        "[<img align=\"left\" src=\"https://cdn-icons-png.flaticon.com/512/145/145807.png\" width=\"32px\"/>][linkedin]\n",
        "[<img align=\"left\" src=\"https://cdn2.iconfinder.com/data/icons/whcompare-blue-green-web-hosting-1/425/cdn-512.png\" width=\"32px\"/>][Portfolio]\n",
        "\n",
        "[twitter]: https://twitter.com/F4izy\n",
        "[linkedin]: https://www.linkedin.com/in/mohd-faizy/\n",
        "[Portfolio]: https://mohdfaizy.com/\n"
      ],
      "metadata": {
        "id": "g4P8Tf4SG-L0"
      }
    }
  ],
  "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": "3.7.6"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}