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Emotion intensities in Tweets

Abstract
This 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.
Type
Conference Contribution
Type of thesis
Series
Citation
Mohammad, S. M., & Bravo-Marquez, F. (2017). Emotion intensities in Tweets. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017) (pp. 65–77). Vancouver, Canada: Association for Computational Linguistics. https://doi.org/10.18653/v1/S17-1007
Date
2017
Publisher
Association for Computational Linguistics
Degree
Supervisors
Rights
This is an author’s accepted version of an article published in the Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017). © 2017 Association for Computational Linguistic