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Natural language processing for social media (3rd ed.)
Farzindar A., Inkpen D., Morgan & Claypool, San Rafael, CA, 2020. 219 pp. Type: Book (978-1-681738-13-0)
Date Reviewed: Nov 19 2020

Social media platforms are not only a place for individuals to voice their opinions, but have also become very influential in recent democratic elections and geo-politics. The importance of social media, including applications to analyze social media posts, is increasing rapidly. Natural language processing (NLP) techniques have been used to solve such applications, and that makes this book very relevant.

The first chapter sets the stage for social media analysis by emphasizing the growing number of users and the challenges to text analysis. The subsequent chapters align with the steps for developing an NLP application: pre-processing techniques (tokenization, part-of-speech, and named entity recognition), data collection and annotations, as well as semantic analysis. The chapter dedicated to semantic analysis--probably the meat of the book--discusses various methods, tools, and techniques for various applications, including opinion mining, geo-location identification, and machine translation. The authors also discuss some concrete domain-specific use cases (such as healthcare, financial, and disaster response applications) to highlight the domain-specific techniques employed along with NLP techniques.

I like the book’s extensive coverage from an academic perspective. The authors outline major research attempts in the field and present them in categories. It provides a comprehensive starting point for academic researchers and students. On the other hand, the book is a monograph and often looks like an extended version of related work in a typical academic paper. Therefore, it is not an ideal choice for individuals who want to understand NLP techniques and try their hand at some examples. The authors kept the book very close to a dull academic read, that is, without any visualization elements; the presented plots and tables are from existing papers. The book also does poorly when it comes to contemporary information; for example, the temporal axes in chapter 1’s figures on the exploding nature of social media cover the duration between 2008 and 2014. The same can be observed with reporting research articles--the majority of cited articles are from 2010 to 2015. Given that NLP research on social media has only increased in the last decade, a lot more fresh content was expected.

To summarize, the book nicely covers the topic of NLP research for social media applications. It could be handy for academic researchers, but offers little value to a non-academic audience.

Reviewer:  Tushar Sharma Review #: CR147113 (2104-0074)
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Natural Language Processing (I.2.7 )
 
 
Social Networking (H.3.4 ... )
 
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