Emotion Intensities in Tweets

Saif Mohammad, Felipe Bravo-Marquez

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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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/S17-1007.pdf
Code
 felipebravom/AffectiveTweets
Data
11k Hands