Team ISCL_WINTER at SemEval-2023 Task 12:AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset

Alina Hancharova, John Wang, Mayank Kumar

Abstract
This paper presents a study on the effectiveness of various approaches for addressing the challenge of multilingual sentiment analysis in low-resource African languages. . The approaches evaluated in the study include Support Vector Machines (SVM), translation, and an ensemble of pre-trained multilingual sentimental models methods. The paper provides a detailed analysis of the performance of each approach based on experimental results. In our findings, we suggest that the ensemble method is the most effective with an F1-Score of 0.68 on the final testing. This system ranked 19 out of 33 participants in the competition.
Anthology ID:
2023.semeval-1.149
Volume:
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1085–1089
Language:
URL:
https://aclanthology.org/2023.semeval-1.149
DOI:
10.18653/v1/2023.semeval-1.149
Bibkey:
Cite (ACL):
Alina Hancharova, John Wang, and Mayank Kumar. 2023. Team ISCL_WINTER at SemEval-2023 Task 12:AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1085–1089, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Team ISCL_WINTER at SemEval-2023 Task 12:AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset (Hancharova et al., SemEval 2023)
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