@inproceedings{hancharova-etal-2023-team,
title = "Team {ISCL}{\_}{WINTER} at {S}em{E}val-2023 Task 12:{A}fri{S}enti-{S}em{E}val: Sentiment Analysis for Low-resource {A}frican Languages using {T}witter Dataset",
author = "Hancharova, Alina and
Wang, John and
Kumar, Mayank",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.149",
doi = "10.18653/v1/2023.semeval-1.149",
pages = "1085--1089",
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.",
}
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%0 Conference Proceedings
%T Team ISCL_WINTER at SemEval-2023 Task 12:AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset
%A Hancharova, Alina
%A Wang, John
%A Kumar, Mayank
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F hancharova-etal-2023-team
%X 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.
%R 10.18653/v1/2023.semeval-1.149
%U https://aclanthology.org/2023.semeval-1.149
%U https://doi.org/10.18653/v1/2023.semeval-1.149
%P 1085-1089
Markdown (Informal)
[Team ISCL_WINTER at SemEval-2023 Task 12:AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset](https://aclanthology.org/2023.semeval-1.149) (Hancharova et al., SemEval 2023)
ACL