Emotion intensities in tweets

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TypeArticle
Journal titleComputer Science
Article numberarXiv:1708.03696
AbstractThis paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best--worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity, and, the extent to which two emotions are similar in terms of how they manifest in language.
Publication date
PublisherCornell University Library
Linkhttps://arxiv.org/abs/1708.03696
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedNo
NPARC number23002133
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Record identifierce218860-e351-49fc-8443-f227d219fe59
Record created2017-08-24
Record modified2017-08-24
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