@inproceedings{rathi-etal-2023-trinity,
title = "Trinity at {S}em{E}val-2023 Task 12: Sentiment Analysis for Low-resource {A}frican Languages using {T}witter Dataset",
author = "Rathi, Shashank and
Pande, Siddhesh and
Atkare, Harshwardhan and
Tangsali, Rahul and
Vyawahare, Aditya and
Kadam, Dipali",
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-1.161",
doi = "10.18653/v1/2023.semeval-1.161",
pages = "1161--1165",
abstract = "In this paper, we have performed sentiment analysis on three African languages (Hausa, Swahili, and Yoruba). We used various deep learning and traditional models paired with a vectorizer for classification and data -preprocessing. We have also used a few data oversampling methods to handle the imbalanced text data. Thus, we could analyze the performance of those models in all the languages by using weighted and macro F1 scores as evaluation metrics.",
}
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%0 Conference Proceedings
%T Trinity at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages using Twitter Dataset
%A Rathi, Shashank
%A Pande, Siddhesh
%A Atkare, Harshwardhan
%A Tangsali, Rahul
%A Vyawahare, Aditya
%A Kadam, Dipali
%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 rathi-etal-2023-trinity
%X In this paper, we have performed sentiment analysis on three African languages (Hausa, Swahili, and Yoruba). We used various deep learning and traditional models paired with a vectorizer for classification and data -preprocessing. We have also used a few data oversampling methods to handle the imbalanced text data. Thus, we could analyze the performance of those models in all the languages by using weighted and macro F1 scores as evaluation metrics.
%R 10.18653/v1/2023.semeval-1.161
%U https://aclanthology.org/2023.semeval-1.161
%U https://doi.org/10.18653/v1/2023.semeval-1.161
%P 1161-1165
Markdown (Informal)
[Trinity at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages using Twitter Dataset](https://aclanthology.org/2023.semeval-1.161) (Rathi et al., SemEval 2023)
ACL