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A Practical Guide to Sentiment AnalysisChallenges in Sentiment Analysis

A Practical Guide to Sentiment Analysis: Challenges in Sentiment Analysis [A vast majority of the work in Sentiment Analysis has been on developing more accurate sentiment classifiers, usually involving supervised machine learning algorithms and a battery of features. Surveys by Pang and Lee (Found Trends Inf Retr 2(12):1135, 2008), Liu and Zhang (A survey of opinion mining and sentiment analysis. In: Aggarwal CC, Zhai C (eds) In: Mining text data. Springer, New York, pp 415463, 2012), and Mohammad (Mohammad Sentiment analysis: detecting valence, emotions, and other effectual states from text. In: Meiselman H (ed) Emotion measurement. Elsevier, Amsterdam, 2016b) give summaries of the many automatic classifiers, features, and datasets used to detect sentiment. In this chapter, we flesh out some of the challenges that still remain, questions that have not been explored sufficiently, and new issues emerging from taking on new sentiment analysis problems. We also discuss proposals to deal with these challenges. The goal of this chapter is to equip researchers and practitioners with pointers to the latest developments in sentiment analysis and encourage more work in the diverse landscape of problems, especially those areas that are relatively less explored.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Practical Guide to Sentiment AnalysisChallenges in Sentiment Analysis

Part of the Socio-Affective Computing Book Series (volume 5)
Editors: Cambria, Erik; Das, Dipankar; Bandyopadhyay, Sivaji; Feraco, Antonio

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References (124)

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
61 –83
DOI
10.1007/978-3-319-55394-8_4
Publisher site
See Chapter on Publisher Site

Abstract

[A vast majority of the work in Sentiment Analysis has been on developing more accurate sentiment classifiers, usually involving supervised machine learning algorithms and a battery of features. Surveys by Pang and Lee (Found Trends Inf Retr 2(12):1135, 2008), Liu and Zhang (A survey of opinion mining and sentiment analysis. In: Aggarwal CC, Zhai C (eds) In: Mining text data. Springer, New York, pp 415463, 2012), and Mohammad (Mohammad Sentiment analysis: detecting valence, emotions, and other effectual states from text. In: Meiselman H (ed) Emotion measurement. Elsevier, Amsterdam, 2016b) give summaries of the many automatic classifiers, features, and datasets used to detect sentiment. In this chapter, we flesh out some of the challenges that still remain, questions that have not been explored sufficiently, and new issues emerging from taking on new sentiment analysis problems. We also discuss proposals to deal with these challenges. The goal of this chapter is to equip researchers and practitioners with pointers to the latest developments in sentiment analysis and encourage more work in the diverse landscape of problems, especially those areas that are relatively less explored.]

Published: Apr 12, 2017

Keywords: Sentiment analysis tasks; Sentiment of the writer, reader, and other entities; Sentiment towards aspects of an entity; Stance detection; Sentiment lexicons; Sentiment annotation; Multilingual sentiment analysis

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