Overview
- Offers an easy-to-follow introduction to text analytics in business and management
- Helps business practitioners increase their accessibility of information available in unstructured text data without requiring extensive coding experience or knowledge in the area
- Includes real-world examples, in multiple programming languages including SAS, R, Statistica, Python and QDA Miner
Part of the book series: Advances in Analytics and Data Science (AADS, volume 2)
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Table of contents (16 chapters)
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Front Matter
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Planning the Text Analytics Project
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Front Matter
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Text Preparation
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Front Matter
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Communicating the Results
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Front Matter
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Text Analytics Examples
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Front Matter
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Keywords
- Text parsing
- Parsing
- Text analytics methodology
- Topic extraction
- Text visualization
- Text analytics software
- Text analytics algorithms
- Text classification
- Classification models
- Tag clouds
- Sentiment tracking
- Content analysis perspectives
- Text mining
- Automated Content Analysis
- Theme Extraction
- Corpus Generation
- Singular Value Decomposition
- Unstructured Data Analysis
About this book
This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media.
Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.
Reviews
Authors and Affiliations
About the authors
Murugan Anandarajan is a Professor of MIS at Drexel University. His current research interests lie in the intersections of areas Crime, IoT, and Analytics. His work has been published in journals such as Decision Sciences, Journal of MIS, and Journal of International Business Studies. He co-authored eight books, including Internet and Workplace Transformation (2006) and its follow up volume, The Internet of People, Things and Services (2018). He has been awarded over $2.5 million in research grants from various government agencies including the National Science Foundation, U.S. Department of Justice, National Institute of Justice, and the State of PA.
Chelsey Hill is an Assistant Professor of Business Analytics in the Information Management and Business Analytics Department of the Feliciano School of Business at Montclair State University. She holds a BA in Political Science from The College of New Jersey, an MS in Business Intelligence from Saint Joseph’sUniversity and a PhD in Business Administration with a concentration in Decision Sciences from Drexel University. Her research interests include consumer product recalls, online consumer reviews, safety and security, public policy and humanitarian operations. Her research has been published in Journal of Informetrics and the International Journal of Business Intelligence Research.
Tom Nolan completed his undergraduate work at Kenyon College. After Kenyon, he attended Drexel University where he graduated with a M.S. in Business Analytics. From there, he worked at Independence Blue Cross in Philadelphia, PA and Anthem Inc. in Houston, TX. Currently, he works with all types of data as a Data Scientist for Mercury Data Science.
Bibliographic Information
Book Title: Practical Text Analytics
Book Subtitle: Maximizing the Value of Text Data
Authors: Murugan Anandarajan, Chelsey Hill, Thomas Nolan
Series Title: Advances in Analytics and Data Science
DOI: https://doi.org/10.1007/978-3-319-95663-3
Publisher: Springer Cham
eBook Packages: Business and Management, Business and Management (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-319-95662-6Published: 03 November 2018
Softcover ISBN: 978-3-030-07080-9Published: 21 December 2018
eBook ISBN: 978-3-319-95663-3Published: 19 October 2018
Series ISSN: 2522-0233
Series E-ISSN: 2522-0241
Edition Number: 1
Number of Pages: XXVIII, 285
Number of Illustrations: 108 b/w illustrations, 157 illustrations in colour
Topics: Big Data/Analytics, Business Information Systems, Statistics for Business, Management, Economics, Finance, Insurance