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Paper Details


Title
Z-Index at BLP-2023 Task 2: A Comparative Study on Sentiment Analysis

Author
Prerona Tarannum, Krishno Dey, Sheak Rashed Haider Noori,

Email

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.


Keywords

Journal or Conference Name
BLP 2023 - 1st Workshop on Bangla Language Processing, Proceedings of the Workshop

Publication Year
2023

Indexing
scopus