NLP Text Processing and Analysis

This repository contains a comprehensive guide to natural language processing (NLP) text processing and analysis. It covers the following topics:

• Text Pre-processing
• Text Representation
• Word2Vec
• Text Classification
• POS Tagging

Text Pre-processing

Text pre-processing is the first and crucial step in NLP. It involves cleaning and transforming the raw text data into a format that can be easily analyzed. This section provides a detailed explanation of text pre-processing techniques, including:

• Lowercasing
• Removing punctuations and special characters
• Removing stop words
• Stemming and Lemmatization
• Removing HTML tags

Text Representation

In NLP, text data needs to be transformed into numerical vectors for analysis. This section provides an overview of text representation techniques, including:

• One-hot encoding
• Term frequency-inverse document frequency (TF-IDF)
• Word Embeddings

Word2Vec

Word2Vec is a widely used word embedding technique that represents words in a high-dimensional vector space. This section provides a step-by-step guide to training and using Word2Vec models.

Text Classification

Text classification is a popular NLP task that involves classifying text data into different categories based on its content. This section provides an in-depth explanation of text classification, including:

• Types of text classification
• Text feature extraction
• Model selection and training
• Evaluation metrics

POS Tagging

POS (part-of-speech) tagging is the process of marking each word in a text with its corresponding part of speech. This section provides an introduction to POS tagging, including:

• What is POS tagging
• POS tag sets
• POS tagging algorithms

Conclusion

This repository provides a comprehensive guide to NLP text processing and analysis, covering the essential topics in the field. Whether you're a beginner or an experienced NLP practitioner, this repository will help you improve your skills and knowledge.