@inproceedings{ramanathan-etal-2023-techssn,
title = "{T}ech{SSN} at {S}em{E}val-2023 Task 12: Monolingual Sentiment Classification in {H}ausa Tweets",
author = "Ramanathan, Nishaanth and
Sivanaiah, Rajalakshmi and
S, Angel Deborah and
Thanka Nadar Thanagathai, Mirnalinee",
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-1.165",
doi = "10.18653/v1/2023.semeval-1.165",
pages = "1190--1194",
abstract = "This paper elaborates on our work in designing a system for SemEval 2023 Task 12: AfriSentiSemEval, which involves sentiment analysis for low-resource African languages using the Twitter dataset. We utilised a pre-trained model to perform sentiment classification in Hausa language tweets. We used a multilingual version of the roBERTa model, which is pretrained on 100 languages, to classify sentiments in Hausa. To tokenize the text, we used the AfriBERTa model, which is specifically pretrained on African languages.",
}
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<abstract>This paper elaborates on our work in designing a system for SemEval 2023 Task 12: AfriSentiSemEval, which involves sentiment analysis for low-resource African languages using the Twitter dataset. We utilised a pre-trained model to perform sentiment classification in Hausa language tweets. We used a multilingual version of the roBERTa model, which is pretrained on 100 languages, to classify sentiments in Hausa. To tokenize the text, we used the AfriBERTa model, which is specifically pretrained on African languages.</abstract>
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%0 Conference Proceedings
%T TechSSN at SemEval-2023 Task 12: Monolingual Sentiment Classification in Hausa Tweets
%A Ramanathan, Nishaanth
%A Sivanaiah, Rajalakshmi
%A S, Angel Deborah
%A Thanka Nadar Thanagathai, Mirnalinee
%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 ramanathan-etal-2023-techssn
%X This paper elaborates on our work in designing a system for SemEval 2023 Task 12: AfriSentiSemEval, which involves sentiment analysis for low-resource African languages using the Twitter dataset. We utilised a pre-trained model to perform sentiment classification in Hausa language tweets. We used a multilingual version of the roBERTa model, which is pretrained on 100 languages, to classify sentiments in Hausa. To tokenize the text, we used the AfriBERTa model, which is specifically pretrained on African languages.
%R 10.18653/v1/2023.semeval-1.165
%U https://aclanthology.org/2023.semeval-1.165
%U https://doi.org/10.18653/v1/2023.semeval-1.165
%P 1190-1194
Markdown (Informal)
[TechSSN at SemEval-2023 Task 12: Monolingual Sentiment Classification in Hausa Tweets](https://aclanthology.org/2023.semeval-1.165) (Ramanathan et al., SemEval 2023)
ACL