Emotion Intensities in Tweets

Saif Mohammad, Felipe Bravo-Marquez


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
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
65–77
Language:
URL:
https://www.aclweb.org/anthology/S17-1007
DOI:
10.18653/v1/S17-1007
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-1007.pdf