NLP with NLTK in Python

A hands-on guide to Natural Language Processing using Python & NLTK

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🚀 Overview

This repository is designed as a comprehensive, executable walkthrough of core NLP concepts using Python’s NLTK library. It includes code examples, explanations, and sample outputs covering key NLP tasks such as tokenization, stopwords, stemming, lemmatization, corpora, WordNet exploration, feature extraction, sentiment analysis, and text classification with machine learning.

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✅ Who Should Visit / Use This Repo

This repository is especially useful for:

• Students & learners who are beginning with natural language processing and want structured, hands-on examples.
• Data scientists / ML practitioners who want quick reference implementations of common NLP tasks using NLTK.
• Instructors / educators who might use this as a template or teaching resource.
• Anyone who wants to refresh their NLP fundamentals or see how basic pipelines are constructed from scratch.

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📂 Repository Structure & Key Modules

Here’s a breakdown of the major sections (files / folders) and what each one does:

File / Module	Description
01-tokenzing-words-sentences	Code & explanation: how to split text into words/sentences (tokenization)
02-stopwords	Handling and filtering stopwords in text
03-stemming-words	Applying different stemming algorithms to terms
04-part-of-speech-tagging	POS tagging — labelling tokens with grammatical roles
05-chunking	Chunking of phrases or subtrees
06-chinking	The opposite of chunking — excluding substructures
07-named-entity-recognition	Recognizing named entities (people, places, organizations)
08-lemmatization	Normalizing words to their lemma form
09-corpora	Working with text corpora, loading built-in/externally sourced corpora
10-wordNet	Exploring synonyms, antonyms, hypernyms using WordNet
11-text-classification	Building simple text classifiers (e.g. spam detection, sentiment)
12-converting-words-to-features	Turning tokenized text into feature representations (bag-of-words, etc.)
13-naive-bayes-classifier	Building & evaluating a naive Bayes classifier on textual features
14-saving-model-pickel	Persisting trained models / objects via pickle
15-scikit-learn-sklearn	Integration / comparison with scikit-learn models
LICENSE	MIT License declaration
(Potential additional README / docs)	Project-level documentation, usage instructions, contributions, etc.

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📌 Key Concepts & Takeaways

These are the core lessons and functionalities this repo demonstrates:

• Tokenization & segmentation: How to split raw text into meaningful units (words, sentences).
• Stopword filtering: Removing common “noise” words that contribute little to meaning.
• Stemming vs Lemmatization: Reducing words to root / lemma forms and knowing when to use which.
• POS tagging, chunking, chinking: Understanding grammatical structure of sentences.
• Named Entity Recognition (NER): Identifying real-world entities in text.
• Corpora & WordNet exploration: Using NLTK’s built-in corpora, lexicons, synonym/antonym networks.
• Feature engineering for text: Converting text into numerical features (e.g. bag-of-words, frequency distributions).
• Text classification / supervised learning: Building classification models for sentiment, spam, etc.
• Model persistence & interoperability: Saving models for reuse, integrating with scikit-learn.
• Hands-on, example-driven approach: Each concept is illustrated via runnable Python scripts and sample outputs — not just theory.

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🛠️ Setup / Usage Instructions

Here’s how a user can clone and run:

1. Clone the repository

   git clone https://github.com/basit-afridi62/nlp-nltk-python.git
   cd nlp-nltk-python