@inproceedings{fatema-etal-2023-emptymind,
title = "{E}mpty{M}ind at {BLP}-2023 Task 2: Sentiment Analysis of {B}angla Social Media Posts using Transformer-Based Models",
author = "Fatema, Karnis and
Das, Udoy and
Mia, Md Ayon and
Mowla, Md Sajidul and
Yahan, Mahshar and
Ullah, Md Fayez and
Sarker, Arpita and
Murad, Hasan",
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-1.39/",
doi = "10.18653/v1/2023.banglalp-1.39",
pages = "300--304",
abstract = "With the popularity of social media platforms, people are sharing their individual thoughts by posting, commenting, and messaging with their friends, which generates a significant amount of digital text data every day. Conducting sentiment analysis of social media content is a vibrant research domain within the realm of Natural Language Processing (NLP), and it has practical, real-world uses. Numerous prior studies have focused on sentiment analysis for languages that have abundant linguistic resources, such as English. However, limited prior research works have been done for automatic sentiment analysis in low-resource languages like Bangla. In this research work, we are going to finetune different transformer-based models for Bangla sentiment analysis. To train and evaluate the model, we have utilized a dataset provided in a shared task organized by the BLP Workshop co-located with EMNLP-2023. Moreover, we have conducted a comparative study among different machine learning models, deep learning models, and transformer-based models for Bangla sentiment analysis. Our findings show that the BanglaBERT (Large) model has achieved the best result with a micro F1-Score of 0.7109 and secured 7th position in the shared task 2 leaderboard of the BLP Workshop in EMNLP 2023."
}
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<abstract>With the popularity of social media platforms, people are sharing their individual thoughts by posting, commenting, and messaging with their friends, which generates a significant amount of digital text data every day. Conducting sentiment analysis of social media content is a vibrant research domain within the realm of Natural Language Processing (NLP), and it has practical, real-world uses. Numerous prior studies have focused on sentiment analysis for languages that have abundant linguistic resources, such as English. However, limited prior research works have been done for automatic sentiment analysis in low-resource languages like Bangla. In this research work, we are going to finetune different transformer-based models for Bangla sentiment analysis. To train and evaluate the model, we have utilized a dataset provided in a shared task organized by the BLP Workshop co-located with EMNLP-2023. Moreover, we have conducted a comparative study among different machine learning models, deep learning models, and transformer-based models for Bangla sentiment analysis. Our findings show that the BanglaBERT (Large) model has achieved the best result with a micro F1-Score of 0.7109 and secured 7th position in the shared task 2 leaderboard of the BLP Workshop in EMNLP 2023.</abstract>
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%0 Conference Proceedings
%T EmptyMind at BLP-2023 Task 2: Sentiment Analysis of Bangla Social Media Posts using Transformer-Based Models
%A Fatema, Karnis
%A Das, Udoy
%A Mia, Md Ayon
%A Mowla, Md Sajidul
%A Yahan, Mahshar
%A Ullah, Md Fayez
%A Sarker, Arpita
%A Murad, Hasan
%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 fatema-etal-2023-emptymind
%X With the popularity of social media platforms, people are sharing their individual thoughts by posting, commenting, and messaging with their friends, which generates a significant amount of digital text data every day. Conducting sentiment analysis of social media content is a vibrant research domain within the realm of Natural Language Processing (NLP), and it has practical, real-world uses. Numerous prior studies have focused on sentiment analysis for languages that have abundant linguistic resources, such as English. However, limited prior research works have been done for automatic sentiment analysis in low-resource languages like Bangla. In this research work, we are going to finetune different transformer-based models for Bangla sentiment analysis. To train and evaluate the model, we have utilized a dataset provided in a shared task organized by the BLP Workshop co-located with EMNLP-2023. Moreover, we have conducted a comparative study among different machine learning models, deep learning models, and transformer-based models for Bangla sentiment analysis. Our findings show that the BanglaBERT (Large) model has achieved the best result with a micro F1-Score of 0.7109 and secured 7th position in the shared task 2 leaderboard of the BLP Workshop in EMNLP 2023.
%R 10.18653/v1/2023.banglalp-1.39
%U https://aclanthology.org/2023.banglalp-1.39/
%U https://doi.org/10.18653/v1/2023.banglalp-1.39
%P 300-304
Markdown (Informal)
[EmptyMind at BLP-2023 Task 2: Sentiment Analysis of Bangla Social Media Posts using Transformer-Based Models](https://aclanthology.org/2023.banglalp-1.39/) (Fatema et al., BanglaLP 2023)
ACL