Practical Text Analytics

Maximizing the Value of Text Data

AvMurugan Anandarajan,Chelsey Hill

Inbunden, Engelska, 2018

Del 2 i serien Advances in Analytics and Data Science

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2018-11-03
Mått:155 x 235 x 23 mm
Vikt:641 g
Format:Inbunden
Språk:Engelska
Serie:Advances in Analytics and Data Science
Antal sidor:285
Förlag:Springer International Publishing AG
ISBN:9783319956626

Utforska kategorier

Databaser inom Data och IT

Mer om författaren

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.

Recensioner i media

“The book also eases readers’ comprehension by presenting a short introduction and illustrating some key takeaways … . This book is relatively suitable for newcomers in the field of text data analytics, especially for students and researchers who do not excel in statistics but still hope to use big data to lay a sound foundation for their research. Hopefully, the book can elucidate readers to step into the wonderful world of text data analytics.” (Jianwei Qian and Rob Law, Information Technology & Tourism, Vol. 21, 2019)

Innehållsförteckning

Chapter 1. Introduction to Text Analytics.- Chapter 2. Fundamentals of Content Analysis.- Chapter 3. Text Analytics Roadmap.- Chapter 4. Text Pre-Processing.- Chapter 5. Term-Document Representation.- Chapter 6. Semantic Space Representation and Latent Semantic Analysis.- Chapter 7. Cluster Analysis: Modeling Groups in Text.- Chapter 8. Probabilistic Topic Models.- Chapter 9. Classification Analysis: Machine Learning Applied to Text.- Chapter 10. Modeling Text Sentiment: Learning and Lexicon Models.- Chapter 11. Storytelling Using Text Data.- Chapter 12. Visualizing Results.- Chapter 13. Sentiment Analysis of Movie Reviews using R.- Chapter 14. Latent Semantic Analysis (LSA) in Python.- Chapter 15. Learning-Based Sentiment Analysis using RapidMiner.- Chapter 16. SAS Visual Text Analytics.