Overview
- 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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Table of contents (10 chapters)
Keywords
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.
Authors and Affiliations
About the author
Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, statistical methods and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. He also acts as a key contributor and Editor for Towards Data Science, a leading online journal focusing on Artificial Intelligence and Data Science. Dipanjan has also authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing and Deep Learning.
Dipanjan's interests include learning about new technology, financial markets, disruptive start-ups, data science, artificial intelligence and deep learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https://medium.com/@dipanzan.sarkar and https://www.linkedin.com/in/dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https://github.com/dipanjanS.
Bibliographic Information
Book Title: Text Analytics with Python
Book Subtitle: A Practitioner's Guide to Natural Language Processing
Authors: Dipanjan Sarkar
DOI: https://doi.org/10.1007/978-1-4842-4354-1
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Dipanjan Sarkar 2019
Softcover ISBN: 978-1-4842-4353-4Published: 22 May 2019
eBook ISBN: 978-1-4842-4354-1Published: 21 May 2019
Edition Number: 2
Number of Pages: XXIV, 674
Number of Illustrations: 189 b/w illustrations
Topics: Artificial Intelligence, Python, Big Data