Summary

In this chapter, we have explored the complete workflow of analyzing text data, from preprocessing unstructured text to building ML and transformer-based models for classification. We began by understanding the unique challenges of textual data and the importance of linguistic preprocessing, including cleaning, tokenization, stopword removal, lemmatization, and POS tagging. We then examined text transformation and enhancement techniques such as bag-of-words, n-grams, and TF-IDF weighting, and explained word embeddings and MLM, which represent textual information with increasing precision.

Then, we implemented text classification models using classical ML and transformer-based contextual embeddings. We also discussed the evaluation methods for text classification to assess model performance.

Text analysis is extremely valuable for practical applications such as sentiment analysis, fraud detection, and additional relevant tasks. By employing detailed examples and adhering...