Text analytics with python : a practical real-world approach to gaining actionable insights from your data / by Dipanjan Sarkar.

By:

Sarkar, Dipanjan [Author.]

Material type: Text

TextLanguage: English Publication details: Karnataka : Apress, 2019.Edition: 2nd edDescription: xxiv, 674 p. ; ill. ; 24 cmISBN:

9781484243534

Subject(s):

DDC classification:

006.35 SAR/T

Contents:

Introduction -- Natural language basics -- Python refresher -- Processing and understanding text -- Text classification -- Text summarization -- Text similarity and clustering -- Semantic and sentiment analysis.

Summary: The second edition of this book focuses on Natural Language Processing (NLP) in Python and how to set up a robust environment for text analytics. It introduces state-of-the-art frameworks in NLP, along with Machine Learning and Deep Learning, to solve real-world case studies. The book includes a dedicated chapter on Python for NLP, feature engineering representation methods, and techniques for parsing and processing text data. It also covers text classification, text summarization, text similarity techniques, sentiment analysis, machine learning and deep learning models, semantic analysis, and deep transfer learning. The book will be updated to the latest Python 3.x release, showcasing diverse NLP applications such as classification, clustering, similarity recommenders, topic models, sentiment and semantic analysis.

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Holdings
Item type	Current library	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Technical Reference Book	Central Library, IIT Bhubaneswar	Central Library, IIT Bhubaneswar	SES	006.35 SAR/T (Browse shelf(Opens below))	Available		10908

Total holds: 0

Includes bibliographical references and index.

The second edition of this book focuses on Natural Language Processing (NLP) in Python and how to set up a robust environment for text analytics. It introduces state-of-the-art frameworks in NLP, along with Machine Learning and Deep Learning, to solve real-world case studies. The book includes a dedicated chapter on Python for NLP, feature engineering representation methods, and techniques for parsing and processing text data. It also covers text classification, text summarization, text similarity techniques, sentiment analysis, machine learning and deep learning models, semantic analysis, and deep transfer learning. The book will be updated to the latest Python 3.x release, showcasing diverse NLP applications such as classification, clustering, similarity recommenders, topic models, sentiment and semantic analysis.

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