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.- Anthology ID:
- S17-1007
- Volume:
- Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Nancy Ide, Aurélie Herbelot, Lluís Màrquez
- Venue:
- *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 65–77
- Language:
- URL:
- https://aclanthology.org/S17-1007
- DOI:
- 10.18653/v1/S17-1007
- Cite (ACL):
- Saif Mohammad and Felipe Bravo-Marquez. 2017. Emotion Intensities in Tweets. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 65–77, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Emotion Intensities in Tweets (Mohammad & Bravo-Marquez, *SEM 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/S17-1007.pdf
- Code
- felipebravom/AffectiveTweets
- Data
- 11k Hands