Book Image

Natural Language Processing and Computational Linguistics

By : Bhargav Srinivasa-Desikan
Book Image

Natural Language Processing and Computational Linguistics

By: Bhargav Srinivasa-Desikan

Overview of this book

Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.
Table of Contents (22 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Visualizing topic models

Like we have said before, the purpose of topic models is to better understand our textual data - and visualizations are one of the best ways to understand and look at our data. There are multiple ways and techniques to visualize topic models - we will be focusing on the methods implemented and compatible with Gensim, but like we have done throughout the book, we will be providing links and documentation to the other popular topic modeling visualization tools.

One of the most popular topic modeling visualization libraries is LDAvis - an R library build largely on D3, it has been ported to Python as pyLDAvis and is just as nifty in Python and is very well integrated with Gensim as well. It is based on the original paper (LDAvis: A method for visualizing and interpreting topics [19]) by Carson Sievert and Kenneth E. Shirley.

The pyLDAvis library is agnostic...