@inproceedings{dey-etal-2023-semantics-squad,
title = "Semantics Squad at {BLP}-2023 Task 2: Sentiment Analysis of {B}angla Text with Fine Tuned Transformer Based Models",
author = "Dey, Krishno and
Hasan, Md. Arid and
Tarannum, Prerona and
Palma, Francis",
editor = "Alam, Firoj and
Kar, Sudipta and
Chowdhury, Shammur Absar and
Sadeque, Farig and
Amin, Ruhul",
booktitle = "Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.banglalp-1.41",
doi = "10.18653/v1/2023.banglalp-1.41",
pages = "312--316",
abstract = "Sentiment analysis (SA) is a crucial task in natural language processing, especially in contexts with a variety of linguistic features, like Bangla. We participated in BLP-2023 Shared Task 2 on SA of Bangla text. We investigated the performance of six transformer-based models for SA in Bangla on the shared task dataset. We fine-tuned these models and conducted a comprehensive performance evaluation. We ranked 20th on the leaderboard of the shared task with a blind submission that used BanglaBERT Small. BanglaBERT outperformed other models with 71.33{\%} accuracy, and the closest model was BanglaBERT Large, with an accuracy of 70.90{\%}. BanglaBERT consistently outperformed others, demonstrating the benefits of models developed using sizable datasets in Bangla.",
}
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<abstract>Sentiment analysis (SA) is a crucial task in natural language processing, especially in contexts with a variety of linguistic features, like Bangla. We participated in BLP-2023 Shared Task 2 on SA of Bangla text. We investigated the performance of six transformer-based models for SA in Bangla on the shared task dataset. We fine-tuned these models and conducted a comprehensive performance evaluation. We ranked 20th on the leaderboard of the shared task with a blind submission that used BanglaBERT Small. BanglaBERT outperformed other models with 71.33% accuracy, and the closest model was BanglaBERT Large, with an accuracy of 70.90%. BanglaBERT consistently outperformed others, demonstrating the benefits of models developed using sizable datasets in Bangla.</abstract>
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%0 Conference Proceedings
%T Semantics Squad at BLP-2023 Task 2: Sentiment Analysis of Bangla Text with Fine Tuned Transformer Based Models
%A Dey, Krishno
%A Hasan, Md. Arid
%A Tarannum, Prerona
%A Palma, Francis
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Sadeque, Farig
%Y Amin, Ruhul
%S Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F dey-etal-2023-semantics-squad
%X Sentiment analysis (SA) is a crucial task in natural language processing, especially in contexts with a variety of linguistic features, like Bangla. We participated in BLP-2023 Shared Task 2 on SA of Bangla text. We investigated the performance of six transformer-based models for SA in Bangla on the shared task dataset. We fine-tuned these models and conducted a comprehensive performance evaluation. We ranked 20th on the leaderboard of the shared task with a blind submission that used BanglaBERT Small. BanglaBERT outperformed other models with 71.33% accuracy, and the closest model was BanglaBERT Large, with an accuracy of 70.90%. BanglaBERT consistently outperformed others, demonstrating the benefits of models developed using sizable datasets in Bangla.
%R 10.18653/v1/2023.banglalp-1.41
%U https://aclanthology.org/2023.banglalp-1.41
%U https://doi.org/10.18653/v1/2023.banglalp-1.41
%P 312-316
Markdown (Informal)
[Semantics Squad at BLP-2023 Task 2: Sentiment Analysis of Bangla Text with Fine Tuned Transformer Based Models](https://aclanthology.org/2023.banglalp-1.41) (Dey et al., BanglaLP 2023)
ACL