Text cleaning for NLP with Python

Text preprocessing is an essential step in preparing text data for natural language processing (NLP) tasks. It involves a series of techniques aimed at reducing noise in the dataset while retaining relevant information. Key steps include tokenization, normalization, removing unwanted characters and stop words, lemmatization, and stemming. These methods help to simplify text, reduce vocabulary size, and improve model performance on NLP tasks.