@inproceedings{das-etal-2023-team-error,
title = "Team Error Point at {BLP}-2023 Task 2: A Comparative Exploration of Hybrid Deep Learning and Machine Learning Approach for Advanced Sentiment Analysis Techniques.",
author = "Das, Rajesh and
Yeiad, Kabid and
Ajmain, Moshfiqur and
Maowa, Jannatul and
Islam, Mirajul and
Khushbu, Sharun",
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-1.44",
doi = "10.18653/v1/2023.banglalp-1.44",
pages = "331--335",
abstract = "This paper presents a thorough and extensive investigation into the diverse models and techniques utilized for sentiment analysis. What sets this research apart is the deliberate and purposeful incorporation of data augmentation techniques with the goal of improving the efficacy of sentiment analysis in the Bengali language. We systematically explore various approaches, including preprocessing techniques, advancedmodels like Long Short-Term Memory (LSTM) and LSTM-CNN (Convolutional Neural Network) Combine, and traditional machine learning models such as Logistic Regression, Decision Tree, Random Forest, Multi-Naive Bayes, Support Vector Machine, and Stochastic Gradient Descent. Our study highlights the substantial impact of data augmentation on enhancing model accuracy and understanding Bangla sentiment nuances. Additionally, we emphasize the LSTM model{'}s ability to capture long-range correlations in Bangla text. Our system scored 0.4129 and ranked 27th among the participants.",
}
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<abstract>This paper presents a thorough and extensive investigation into the diverse models and techniques utilized for sentiment analysis. What sets this research apart is the deliberate and purposeful incorporation of data augmentation techniques with the goal of improving the efficacy of sentiment analysis in the Bengali language. We systematically explore various approaches, including preprocessing techniques, advancedmodels like Long Short-Term Memory (LSTM) and LSTM-CNN (Convolutional Neural Network) Combine, and traditional machine learning models such as Logistic Regression, Decision Tree, Random Forest, Multi-Naive Bayes, Support Vector Machine, and Stochastic Gradient Descent. Our study highlights the substantial impact of data augmentation on enhancing model accuracy and understanding Bangla sentiment nuances. Additionally, we emphasize the LSTM model’s ability to capture long-range correlations in Bangla text. Our system scored 0.4129 and ranked 27th among the participants.</abstract>
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%0 Conference Proceedings
%T Team Error Point at BLP-2023 Task 2: A Comparative Exploration of Hybrid Deep Learning and Machine Learning Approach for Advanced Sentiment Analysis Techniques.
%A Das, Rajesh
%A Yeiad, Kabid
%A Ajmain, Moshfiqur
%A Maowa, Jannatul
%A Islam, Mirajul
%A Khushbu, Sharun
%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 das-etal-2023-team-error
%X This paper presents a thorough and extensive investigation into the diverse models and techniques utilized for sentiment analysis. What sets this research apart is the deliberate and purposeful incorporation of data augmentation techniques with the goal of improving the efficacy of sentiment analysis in the Bengali language. We systematically explore various approaches, including preprocessing techniques, advancedmodels like Long Short-Term Memory (LSTM) and LSTM-CNN (Convolutional Neural Network) Combine, and traditional machine learning models such as Logistic Regression, Decision Tree, Random Forest, Multi-Naive Bayes, Support Vector Machine, and Stochastic Gradient Descent. Our study highlights the substantial impact of data augmentation on enhancing model accuracy and understanding Bangla sentiment nuances. Additionally, we emphasize the LSTM model’s ability to capture long-range correlations in Bangla text. Our system scored 0.4129 and ranked 27th among the participants.
%R 10.18653/v1/2023.banglalp-1.44
%U https://aclanthology.org/2023.banglalp-1.44
%U https://doi.org/10.18653/v1/2023.banglalp-1.44
%P 331-335
Markdown (Informal)
[Team Error Point at BLP-2023 Task 2: A Comparative Exploration of Hybrid Deep Learning and Machine Learning Approach for Advanced Sentiment Analysis Techniques.](https://aclanthology.org/2023.banglalp-1.44) (Das et al., BanglaLP 2023)
ACL