@inproceedings{tarannum-etal-2023-z,
title = "{Z}-Index at {BLP}-2023 Task 2: A Comparative Study on Sentiment Analysis",
author = "Tarannum, Prerona and
Hasan, Md. Arid and
Dey, Krishno and
Noori, Sheak Rashed Haider",
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-1.43/",
doi = "10.18653/v1/2023.banglalp-1.43",
pages = "324--330",
abstract = "In this study, we report our participation in Task 2 of the BLP-2023 shared task. The main objective of this task is to determine the sentiment (Positive, Neutral, or Negative) of a given text. We first removed the URLs, hashtags, and other noises and then applied traditional and pretrained language models. We submitted multiple systems in the leaderboard and BanglaBERT with tokenized data provided thebest result and we ranked 5th position in the competition with an F1-micro score of 71.64. Our study also reports that the importance of tokenization is lessening in the realm of pretrained language models. In further experiments, our evaluation shows that BanglaBERT outperforms, and predicting the neutral class is still challenging for all the models."
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<abstract>In this study, we report our participation in Task 2 of the BLP-2023 shared task. The main objective of this task is to determine the sentiment (Positive, Neutral, or Negative) of a given text. We first removed the URLs, hashtags, and other noises and then applied traditional and pretrained language models. We submitted multiple systems in the leaderboard and BanglaBERT with tokenized data provided thebest result and we ranked 5th position in the competition with an F1-micro score of 71.64. Our study also reports that the importance of tokenization is lessening in the realm of pretrained language models. In further experiments, our evaluation shows that BanglaBERT outperforms, and predicting the neutral class is still challenging for all the models.</abstract>
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%0 Conference Proceedings
%T Z-Index at BLP-2023 Task 2: A Comparative Study on Sentiment Analysis
%A Tarannum, Prerona
%A Hasan, Md. Arid
%A Dey, Krishno
%A Noori, Sheak Rashed Haider
%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 tarannum-etal-2023-z
%X In this study, we report our participation in Task 2 of the BLP-2023 shared task. The main objective of this task is to determine the sentiment (Positive, Neutral, or Negative) of a given text. We first removed the URLs, hashtags, and other noises and then applied traditional and pretrained language models. We submitted multiple systems in the leaderboard and BanglaBERT with tokenized data provided thebest result and we ranked 5th position in the competition with an F1-micro score of 71.64. Our study also reports that the importance of tokenization is lessening in the realm of pretrained language models. In further experiments, our evaluation shows that BanglaBERT outperforms, and predicting the neutral class is still challenging for all the models.
%R 10.18653/v1/2023.banglalp-1.43
%U https://aclanthology.org/2023.banglalp-1.43/
%U https://doi.org/10.18653/v1/2023.banglalp-1.43
%P 324-330
Markdown (Informal)
[Z-Index at BLP-2023 Task 2: A Comparative Study on Sentiment Analysis](https://aclanthology.org/2023.banglalp-1.43/) (Tarannum et al., BanglaLP 2023)
ACL