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Challenges in Sentiment Analysis
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DOI：10.1007/978-3-319-55394-8_4.png)
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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(1-2): 1-135, 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 415-463, 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.
Keyword:
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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