nltk-nlp-text-cleaning

What problem does it solve?

This skill streamlines preprocessing of raw text for NLP models by providing a structured workflow that cleans, tokenizes, removes stopwords, and optionally stems or lemmatizes text, followed by guidance on vectorization with NLTK.

Core Features & Use Cases

• Cleaning raw text, tokenization, and punctuation handling to prepare data for analysis.
• Stopword removal and optional negation-friendly processing to retain meaningful sentiment cues.
• Stemming or lemmatization to normalize word forms, plus guidance for vectorization (BoW, TF-IDF, and n-grams) to build model-ready features.
• Build reusable preprocessing pipelines for downstream ML tasks and evaluation of NLP model outputs.

Quick Start

Run the NLTK NLP Text Cleaning workflow on a sample corpus to obtain clean, tokenized sentences ready for vectorization.