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

- Chapter 1. Language and Computation
- Chapter 2. Text Ingestion and Wrangling
- Chapter 3. Machine Learning on Text
- Chapter 4. Classification for Text Analysis
- Chapter 5. Clustering for Text Similarity
- Chapter 6. Context-Aware Text Analysis
- Chapter 7. Text Visualization
- Chapter 8. Graph Analysis of Text
- Chapter 9. Chatbots
- Chapter 10. Scaling Text Analytics
- Chapter 11. Language-Aware Data Products
- Appendix A. Installing Libraries and Downloading Corpora