NLP-Bag-of-Words-&-TF-IDF

Overview

This project demonstrates how to preprocess text data using two common Natural Language Processing (NLP) techniques: Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). Both methods are used to convert text documents into numerical feature vectors that can be used for machine learning models and text analysis.

Key Concepts

Bag of Words (BoW)

The Bag of Words model is a simple and widely used method for text representation. It transforms text into a fixed-length vector of word counts, ignoring the order and grammar of words. Here’s a brief overview:

• Vocabulary Creation: Build a vocabulary of all unique words in the entire corpus.
• Vector Representation: For each document, create a vector where each position represents a word from the vocabulary. The value at each position is the count of the word in the document.

Advantages:

• Simple and easy to implement.
• Effective for basic text classification tasks.

Disadvantages:

• Ignores word order and context.
• Can lead to large feature vectors with high dimensionality.

Term Frequency-Inverse Document Frequency (TF-IDF)

TF-IDF is an advanced text representation technique that combines term frequency (TF) and inverse document frequency (IDF) to capture the importance of words in a document relative to a corpus. Here’s how it works:

• Term Frequency (TF): Measures how frequently a term appears in a document. It is calculated as: [ \text{TF}(t, d) = \frac{\text{Number of times term } t \text{ appears in document } d}{\text{Total number of terms in document } d} ]

• Inverse Document Frequency (IDF): Measures how important a term is across all documents. It is calculated as: [ \text{IDF}(t, D) = \log \left( \frac{\text{Total number of documents } N}{\text{Number of documents containing term } t} \right) ]

• TF-IDF: Combines TF and IDF to provide a score for each term in each document: [ \text{TF-IDF}(t, d, D) = \text{TF}(t, d) \times \text{IDF}(t, D) ]

Advantages:

• Considers both term frequency and the rarity of terms.
• Provides a better representation of important words in documents.

Disadvantages:

• More complex than BoW.
• Still ignores word order and context.

Project Structure

• notebooks/: Jupyter notebooks demonstrating the usage of BoW and TF-IDF with examples.
• README.md: This file.

Requirements

• Python 3.x
• nltk
• scikit-learn
• pandas