From Messy Text to Model-Ready Data: A Guide to NLP Preprocessing

NLP preprocessing transforms raw text into structured data ready for machine learning models. Key steps include text cleaning, tokenization, stopword removal, lemmatization, part-of-speech tagging, named entity recognition, and text vectorization. Effective preprocessing enhances model performance, making it crucial for tasks like sentiment analysis, chatbots, and language translation.