Text Analytics with Python: A Practitioner's Guide to Natural Language Processing - Softcover

Sarkar, Dipanjan

Softcover

ISBN 10: 1484243536 ISBN 13: 9781484243534

Publisher: Apress, 2019

Synopsis

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 this title

From the Back Cover

Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python.

This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. Techniques around parsing and processing text data have also been improved with some new methods.

Considering popular NLP applications, for text classification, we also cover methods for tuning and improving our models. Text Summarization has gone through a major overhaul in the context of topic models where we showcase how to build, tune and interpret topic models in the context of an interest dataset on NIPS conference papers. Similarly, we cover text similarity techniques with a real-world example of movie recommenders. Sentiment Analysis is covered in-depth with both supervised and unsupervised techniques. We also cover both machine learning and deep learning models for supervised sentiment analysis. Semantic Analysis gets its own dedicated chapter where we also showcase how you can build your own Named Entity Recognition (NER) system from scratch. To conclude things, we also have a completely new chapter on the promised of Deep Learning for NLP where we also showcase a hands-on example on deep transfer learning.

While the overall structure of the book remains the same, the entire code base, modules, and chapters will be updated to the latest Python 3.x release.----------------------------------Also the key selling points· Implementations are based on Python 3.x and state-of-the-art popular open source libraries in NLP · Covers Machine Learning and Deep Learning for Advanced Text Analytics and NLP· Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment and Semantic Analysis

About the Author

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.

Edition details

• Publisher: Apress
• Publication date: 2019
• ISBN 10: 1484243536
• ISBN 13: 9781484243534
• Binding: Paperback
• Language: English
• Edition number: 2
• Number of pages: 698