EmoTweet-28: A fine-grained emotion corpus for sentiment analysis

Jasy Liew Suet Yan, Howard R. Turtle, Elizabeth D Liddy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

This paper describes EmoTweet-28, a carefully curated corpus of 15,553 tweets annotated with 28 emotion categories for the purpose of training and evaluating machine learning models for emotion classification. EmoTweet-28 is, to date, the largest tweet corpus annotated with fine-grained emotion categories. The corpus contains annotations for four facets of emotion: valence, arousal, emotion category and emotion cues. We first used small-scale content analysis to inductively identify a set of emotion categories that characterize the emotions expressed in microblog text. We then expanded the size of the corpus using crowdsourcing. The corpus encompasses a variety of examples including explicit and implicit expressions of emotions as well as tweets containing multiple emotions. EmoTweet-28 represents an important resource to advance the development and evaluation of more emotion-sensitive systems.

Original languageEnglish (US)
Title of host publicationProceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
PublisherEuropean Language Resources Association (ELRA)
Pages1149-1156
Number of pages8
ISBN (Electronic)9782951740891
StatePublished - Jan 1 2016
Event10th International Conference on Language Resources and Evaluation, LREC 2016 - Portoroz, Slovenia
Duration: May 23 2016May 28 2016

Other

Other10th International Conference on Language Resources and Evaluation, LREC 2016
CountrySlovenia
CityPortoroz
Period5/23/165/28/16

Keywords

  • Emotion corpus
  • Microblog text
  • Sentiment analysis

ASJC Scopus subject areas

  • Linguistics and Language
  • Library and Information Sciences
  • Language and Linguistics
  • Education

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  • Cite this

    Yan, J. L. S., Turtle, H. R., & Liddy, E. D. (2016). EmoTweet-28: A fine-grained emotion corpus for sentiment analysis. In Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016 (pp. 1149-1156). European Language Resources Association (ELRA).