tRF-BERT: A transformative approach to aspect-based sentiment analysis in the bengali language.

Autor: Ahmed S; Department of Information and Communication Technology, Comilla University, Cumilla, Bangladesh., Samia MM; Department of Information and Communication Technology, Comilla University, Cumilla, Bangladesh., Sayma MH; Department of Computer Science and Engineering, CCN University of Science & Technology, Cumilla, Bangladesh., Kabir MM; Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.; Faculty of Informatics, Eötvös Loránd University, Budapest, Hungary., Mridha MF; Department of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Sep 20; Vol. 19 (9), pp. e0308050. Date of Electronic Publication: 2024 Sep 20 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0308050
Abstrakt: In recent years, the surge in reviews and comments on newspapers and social media has made sentiment analysis a focal point of interest for researchers. Sentiment analysis is also gaining popularity in the Bengali language. However, Aspect-Based Sentiment Analysis is considered a difficult task in the Bengali language due to the shortage of perfectly labeled datasets and the complex variations in the Bengali language. This study used two open-source benchmark datasets of the Bengali language, Cricket, and Restaurant, for our Aspect-Based Sentiment Analysis task. The original work was based on the Random Forest, Support Vector Machine, K-Nearest Neighbors, and Convolutional Neural Network models. In this work, we used the Bidirectional Encoder Representations from Transformers, the Robustly Optimized BERT Approach, and our proposed hybrid transformative Random Forest and Bidirectional Encoder Representations from Transformers (tRF-BERT) models to compare the results with the existing work. After comparing the results, we can clearly see that all the models used in our work achieved better results than any of the previous works on the same dataset. Amongst them, our proposed transformative Random Forest and Bidirectional Encoder Representations from Transformers achieved the highest F1 score and accuracy. The accuracy and F1 score of aspect detection for the Cricket dataset were 0.89 and 0.85, respectively, and for the Restaurant dataset were 0.92 and 0.89 respectively.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Ahmed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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