Text cleaning for NLP with Python

This post details text cleaning techniques for NLP tasks in Python, including normalization (lowercasing), removal of unwanted characters (emojis, mentions, URLs, punctuation), stop word removal using NLTK, and both lemmatization and stemming. It provides code examples for each step and combines them into a pipeline function for processing text data, demonstrating its application on a sample dataset.