Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning

Applied Text Analysis with Python is a practical and accessible guide for data scientists, developers, and machine learning practitioners who want to build language-aware applications using Python and NLP techniques.

Written by industry experts Benjamin Bengfort, Rebecca Bilbro, and Tony Ojeda, this book bridges the gap between natural language processing theory and practical implementation. It walks you through the full pipeline of text analytics—from raw data to deployed machine learning models.

You’ll learn to:

• Clean and preprocess unstructured text data

• Visualize linguistic patterns and semantic structures

• Build machine learning models for classification, clustering, and prediction

• Implement topic modeling, sentiment analysis, and document summarization

• Use libraries like NLTK, scikit-learn, spaCy, and Gensim

• Deploy NLP applications for real-world use cases like recommendation systems and search engines

Ideal for those with a basic understanding of Python and machine learning, this book helps readers create systems that understand, interpret, and act on human language.

Whether you’re analyzing customer reviews, mining social media, or automating business insights, this book empowers you to turn text into technology.