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

The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python’s scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding of Python and a basic understanding of machine learning and natural language processing how to become more proficient in these two exciting areas of data science.

This book presents a concise, focused, and applied approach to text analysis with Python, and covers topics including text ingestion and wrangling, basic machine learning on text, classification for text analysis, entity resolution, and text visualization. Applied Text Analysis with Python will enable you to design and develop language-aware data products.

You’ll learn how and why machine learning algorithms make decisions about language to analyze text; how to ingest, wrangle, and preprocess language data; and how the three primary text analysis libraries in Python work in concert. Ultimately, this book will enable you to design and develop language-aware data products.

Table of Contents

Chapter 1. Language And Computation
Chapter 2. Building A Custom Corpus
Chapter 3. Corpus Preprocessing And Wrangling
Chapter 4. Text Vectorization And Transformation Pipelines
Chapter 5. Classification For Text Analysis
Chapter 6. Clustering For Text Similarity
Chapter 7. Context-Aware Text Analysis
Chapter 8. Text Visualization
Chapter 9. Graph Analysis Of Text
Chapter 10. Chatbots
Chapter 11. Scaling Text Analytics With Multiprocessing And Spark
Chapter 12. Deep Learning And Beyond