Identifying Emotional and Polar Concepts via Synset Translation

Logan Woudstra, Moyo Dawodu, Frances Igwe, Senyu Li, Ning Shi, Bradley Hauer, Grzegorz Kondrak


Abstract
Emotion identification and polarity classification seek to determine the sentiment expressed by a writer. Sentiment lexicons that provide classifications at the word level fail to distinguish between different senses of polysemous words. To address this problem, we propose a translation-based method for labeling each individual lexical concept and word sense. Specifically, we translate synsets into 20 different languages and verify the sentiment of these translations in multilingual sentiment lexicons. By applying our method to all WordNet synsets, we produce SentiSynset, a synset-level sentiment resource containing 12,429 emotional synsets and 15,567 polar synsets, which is significantly larger than previous resources. Experimental evaluation shows that our method outperforms prior automated methods that classify word senses, in addition to outperforming ChatGPT. We make the resulting resource publicly available on GitHub.
Anthology ID:
2024.starsem-1.12
Volume:
Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Danushka Bollegala, Vered Shwartz
Venue:
*SEM
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
142–152
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.starsem-1.12/
DOI:
10.18653/v1/2024.starsem-1.12
Bibkey:
Cite (ACL):
Logan Woudstra, Moyo Dawodu, Frances Igwe, Senyu Li, Ning Shi, Bradley Hauer, and Grzegorz Kondrak. 2024. Identifying Emotional and Polar Concepts via Synset Translation. In Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024), pages 142–152, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Identifying Emotional and Polar Concepts via Synset Translation (Woudstra et al., *SEM 2024)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.starsem-1.12.pdf