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Text Analytics with Python

A Practitioner's Guide to Natural Language Processing

Authors: Sarkar, Dipanjan

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  •  Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment, and Semantic Analysis
  • Implementations are based on Python 3.x and several popular open source libraries in NLP 
  • Covers Deep Learning for advanced text analytics and NLP 
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eBook 24,99 €
price for China (P.R.) (gross)
  • ISBN 978-1-4842-4354-1
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 29,99 €
price for China (P.R.) (gross)
  • ISBN 978-1-4842-4353-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. 

You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well.   

Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques.

There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release.


What You'll Learn

• Understand NLP and text syntax, semantics and structure• Discover text cleaning and feature engineering• Review text classification and text clustering • Assess text summarization and topic models• Study deep learning for NLP
Who This Book Is For
IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.

About the authors

Dipanjan Sarkar is a Data Scientist at Intel, the world's largest silicon company which is on a mission to make the world more connected and productive. He primarily works on Analytics, Business Intelligence, Application Development and building large scale Intelligent Systems. He received his master's degree in Information Technology from the International Institute of Information Technology, Bangalore with a focus on Data Science and Software Engineering. He is also an avid supporter of self-learning, especially Massive Open Online Courses and holds a Data Science Specialisation from Johns Hopkins University on Coursera.

He has been an analytics practitioner for over six years, specializing in statistical, predictive and text analytics. He has also authored a books on R and Machine Learning and occasionally reviews technical books and acts as a course beta tester for Coursera. Dipanjan's interests include learning about new technology, financial markets, disruptive start-ups, data science and more recently, artificial intelligence and deep learning. In his spare time he loves reading, gaming and watching popular sitcoms and football.

Table of contents (10 chapters)

Table of contents (10 chapters)

Buy this book

eBook 24,99 €
price for China (P.R.) (gross)
  • ISBN 978-1-4842-4354-1
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 29,99 €
price for China (P.R.) (gross)
  • ISBN 978-1-4842-4353-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Text Analytics with Python
Book Subtitle
A Practitioner's Guide to Natural Language Processing
Authors
Copyright
2019
Publisher
Apress
Copyright Holder
Dipanjan Sarkar
eBook ISBN
978-1-4842-4354-1
DOI
10.1007/978-1-4842-4354-1
Softcover ISBN
978-1-4842-4353-4
Edition Number
2
Number of Pages
XXIV, 674
Number of Illustrations
189 b/w illustrations
Topics