Advancement in Bangla Sentiment Analysis: A Comparative Study of Transformer-Based and Transfer Learning Models for E-commerce Sentiment Classification

Abstract

Background: As a direct result of the Internet's expansion, the quantity of information shared by Internet users across its numerous platforms has increased. Sentiment analysis functions at a higher level when there are more available perspectives and opinions. However, the lack of labeled data significantly complicates sentiment analysis utilizing Bangla natural language processing (NLP). In recent years, nevertheless, due to the development of more effective deep learning models, Bangla sentiment analysis has improved significantly. Objective: This article presents a curated dataset for Bangla e-commerce sentiment analysis obtained solely from the "Daraz" platform. We aim to conduct sentiment analysis in Bangla for binary and understudied multiclass classification tasks. Methods: Transfer learning (LSTM, GRU) and Transformers (Bangla-BERT) approaches are compared for their effectiveness on our dataset. To enhance the overall performance of the models, we fine-tuned them. Results: The accuracy of Bangla-BERT was highest for both binary and multiclass sentiment classification tasks, with 94.5% accuracy for binary classification and 88.78% accuracy for multiclass sentiment classification. Conclusion: Our proposed method performs noticeably better classifying multiclass sentiments in Bangla than previous deep learning techniques. Keywords: Bangla-BERT, Deep Learning, E-commerce, NLP, Sentiment Analysis

Empowering the Edge: Research advances from doors 2024

“…• Olexander Barmak, Khmelnytskyi National University, Ukraine [22,23] • Akinul Islam Jony, American International University-Bangladesh, Bangladesh [2,55] • Valerii Kontsedailo, Inner Circle, Netherlands [45,46] • Vyacheslav Kryzhanivskyy, R&D Seco Tools AB, Sweden [15,33] • Nadiia Lobanchykova, Zhytomyr Polytechnic State University, Ukraine [27,28] • Mykhailo Medvediev, ADA University, Azerbaijan [24,34] • Franco Milano, University of Florence, Italy [31,40] • Tetiana Nikitchuk, Zhytomyr Polytechnic State University, Ukraine [36,38] • Etibar Seyidzade, Baku Engineering University, Azerbaijan [1,14] • Andrii Striuk, Kryvyi Rih National University, Ukraine [51,52] Organizing committee…”

Mining User Opinions: A Balanced Bangla Sentiment Analysis Dataset for E-Commerce

“…The dataset was applied to both machine learning [30] and deep learning models [31] to evaluate the classification accuracy using several evaluation metrics.…”