Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A Practical Guide to Sentiment AnalysisDeception Detection and Opinion Spam

A Practical Guide to Sentiment Analysis: Deception Detection and Opinion Spam [In this chapter we first introduce the reader to the problem of deception detection in general, describing how lies may be detected automatically using different methods. Later we address the specific problem of deception detection in predatory communication. We make emphasis especially on those approaches using affective resources as categorical and psychometric information provided by natural language processing tools. Finally, we focus on the problem of opinion spam whose detection is very important for reliable opinion mining. In fact, nowadays a large number of opinion reviews are posted on the Web. Such reviews are a very important source of information for customers and companies. Unfortunately, due to the business behind it, there is an increasing number of deceptive opinions on the Web. Those opinions are fictitious and have been deliberately written to sound authentic in order to deceive the consumers promoting a low quality product (positive deceptive opinions) or criticizing a potentially good quality one (negative deceptive opinions). Then, we summary some interesting approaches to detect spam opinion on the Web.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Practical Guide to Sentiment AnalysisDeception Detection and Opinion Spam

Part of the Socio-Affective Computing Book Series (volume 5)
Editors: Cambria, Erik; Das, Dipankar; Bandyopadhyay, Sivaji; Feraco, Antonio
Rosso, Paolo; Cagnina, Leticia C.
Springer Journals — Apr 12, 2017
Download PDF
16 pages

Loading next page...
 
/lp/springer-journals/a-practical-guide-to-sentiment-analysis-deception-detection-and-8QpohDOzKd
Publisher
Springer International Publishing
Copyright
© Springer International Publishing AG 2017. Chapter 4 is published with kind permission of the Her Majesty the Queen Right of Canada.
ISBN
978-3-319-55392-4
Pages
155 –171
DOI
10.1007/978-3-319-55394-8_8
Publisher site
See Chapter on Publisher Site

Abstract

[In this chapter we first introduce the reader to the problem of deception detection in general, describing how lies may be detected automatically using different methods. Later we address the specific problem of deception detection in predatory communication. We make emphasis especially on those approaches using affective resources as categorical and psychometric information provided by natural language processing tools. Finally, we focus on the problem of opinion spam whose detection is very important for reliable opinion mining. In fact, nowadays a large number of opinion reviews are posted on the Web. Such reviews are a very important source of information for customers and companies. Unfortunately, due to the business behind it, there is an increasing number of deceptive opinions on the Web. Those opinions are fictitious and have been deliberately written to sound authentic in order to deceive the consumers promoting a low quality product (positive deceptive opinions) or criticizing a potentially good quality one (negative deceptive opinions). Then, we summary some interesting approaches to detect spam opinion on the Web.]

Published: Apr 12, 2017

Keywords: Deception detection; Opinion spam; Lie detection; Online sexual predators detection

There are no references for this article.