Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text

By Saif M. Mohammad. arXiv.org, 2016

368 citations recorded by the source corpus.

Abstract Sentiment analysis is the task of automatically determining from text the attitude, emotion, or some other affectual state of the author. This chapter summarizes the diverse landscape of tasks and applications associated with sentiment analysis. We outline key challenges stemming from the complexity and subtlety of language use, the prevalence of creative and non-standard language, and the lack of paralinguistic information, such as tone and stress markers. We describe automatic systems and datasets commonly used in sentiment analysis. We summarize several manual and automatic approaches to creating valence- and emotion-association lexicons. We also discuss preliminary approaches for sentiment composition (how smaller units of text combine to express sentiment) and approaches for detecting sentiment in figurative and metaphoric language—these are the areas where we expect to see significant work in the near future.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/1b4451592ac0aeda5276e45293e5fd21ff1484ef

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In Artificial Intelligence

Citations in the corpus

SemEval-2018 Task 1: Affect in Tweets (2018, background): “There is a large body of prior work on sentiment and emotion classification (Mohammad, 2016).”