@inproceedings{mohammad-bravo-marquez-2017-emotion,
title = "Emotion Intensities in Tweets",
author = "Mohammad, Saif and
Bravo-Marquez, Felipe",
editor = "Ide, Nancy and
Herbelot, Aur{\'e}lie and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-1007",
doi = "10.18653/v1/S17-1007",
pages = "65--77",
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.",
}
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%0 Conference Proceedings
%T Emotion Intensities in Tweets
%A Mohammad, Saif
%A Bravo-Marquez, Felipe
%Y Ide, Nancy
%Y Herbelot, Aurélie
%Y Màrquez, Lluís
%S Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F mohammad-bravo-marquez-2017-emotion
%X 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.
%R 10.18653/v1/S17-1007
%U https://aclanthology.org/S17-1007
%U https://doi.org/10.18653/v1/S17-1007
%P 65-77
Markdown (Informal)
[Emotion Intensities in Tweets](https://aclanthology.org/S17-1007) (Mohammad & Bravo-Marquez, *SEM 2017)
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.