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
Permanent Research Commons link: https://hdl.handle.net/10289/11513
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.
Association for Computational Linguistics
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