NLP with NLTK in Python 🚀

Welcome to my NLTK NLP project on GitHub! 📚 This repository is a documentation of my hands-on exploration of Natural Language Processing (NLP) concepts and techniques using the NLTK library in Python. Below, I'll walk you through some very very very simple examples I did with the following:

💡 Note: It took me a while to put together this documentation. I hope you find it helpful! 👀

Tokenization 📝

In this phase, I explored the fascinating world of tokenization, where text is sliced into meaningful units called tokens. Here's what I accomplished:

• Learned the Concept: Understood the essence of tokenization and its importance.
• Applied Techniques: Utilized NLTK's nltk.tokenize module to segment text into words and sentences.
• Practical Implementation: Delved into Python code to practice tokenization.
• Exercises and Examples: Worked on hands-on exercise and example with matplotlib library showcased in Tokenization.py.

Stopword Removal 🛑

This phase helped me understand the significance of stopwords and how they impact NLP tasks. My achievements include:

• Identifying Stopwords: Recognized commonly used stopwords and their role in text analysis.
• Removal Techniques: Explored effective strategies to eliminate irrelevant words from text data.
• Python Implementation: Applied NLTK's nltk.corpus.stopwords and text preprocessing techniques.
• Practical Application: Engaged with exercise showcased in Stopword_Removal.py to practice stopword removal.

Parts of Speech Tagging 📊

Diving into grammatical analysis, I focused on understanding parts of speech and their roles. Here's what I achieved:

• Understanding POS: Explored the concept of parts of speech and their grammatical categories.
• POS Tagging: Leveraged NLTK's nltk.pos_tag to assign appropriate tags to words.
• Real-world Application: Implemented parts of speech tagging through practical exercise and example in Parts_of_Speech_Tagging.py.

Named Entity Recognition (NER) 🏙️

Named entities gained my attention as I delved into identifying and extracting various types. Here's a summary of my achievements:

• Significance of NER: Understood the importance of named entities in NLP.
• Types of Entities: Identified different categories like persons, locations, organizations, and dates.
• NER Techniques: Applied NLTK's nltk.ne_chunk to extract named entities from text.
• Hands-on Practice: Engaged in interactive activities and exercises in Named_Entity_Recognition.py to reinforce NER skills.

Sentiment Analysis using VADER 😃😔

Emotions in text fascinated me as I ventured into sentiment analysis using the VADER tool. Here's what I accomplished:

• Understanding Sentiment Analysis: Grasped the role of sentiment analysis in determining emotional polarity ( it was cooool :)
• Introduction to VADER: Explored the Valence Aware Dictionary and Sentiment Reasoner as a pre-trained model.
• Analyzing Sentiment: Applied VADER to analyze text sentiment and interpreted results.
• Practical Exercises: Engaged in hands-on activities in Sentiment_Analysis_using_VADER.py to perform sentiment analysis using VADER.

And most importantly Enjoy the process of learning and discovery! 🌟🐍

Tags: python, nltk, natural language processing, text analysis, tokenization, stopword removal, parts of speech tagging, named entity recognition, sentiment analysis