connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detection

Patrick Hantsch, Nadav Chkroun


Abstract
We investigated the influence of contradictory connotations of words or phrases occurring in sarcastic statements, causing those statements to convey the opposite of their literal meaning. Our approach was to perform a sentiment analysis in order to capture potential opposite sentiments within one sentence and use its results as additional information for a further classifier extracting general text features, testing this for a Convolutional Neural Network, as well as for a Support Vector Machine classifier, respectively.We found that a more complex and sophisticated implementation of the sentiment analysis than just classifying the sentences as positive or negative is necessary, since our implementation showed a worse performance in both approaches than the respective classifier without using any sentiment analysis.
Anthology ID:
2022.semeval-1.132
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
945–950
Language:
URL:
https://aclanthology.org/2022.semeval-1.132
DOI:
10.18653/v1/2022.semeval-1.132
Bibkey:
Cite (ACL):
Patrick Hantsch and Nadav Chkroun. 2022. connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 945–950, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detection (Hantsch & Chkroun, SemEval 2022)
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PDF:
https://preview.aclanthology.org/auto-file-uploads/2022.semeval-1.132.pdf