Social Media Sentiments Analysis on the July Revolution in Bangladesh: A Hybrid Transformer Based Machine Learning Approach

TL;DR

This paper introduces a hybrid transformer-based machine learning framework for sentiment analysis of social media comments in Bangla, specifically applied to the July Revolution in Bangladesh, achieving high accuracy in a low-resource language context.

Contribution

The study presents a novel hybrid transformer model, XMB-BERT, combined with a voting classifier, for effective sentiment analysis in Bangla social media data, outperforming existing models.

Findings

Hybrid XMB-BERT achieved 83.7% accuracy.

Transformer-based features effectively captured nuanced sentiments.

The approach demonstrates potential for low-resource language sentiment analysis.

Abstract

The July Revolution in Bangladesh marked a significant student-led mass uprising, uniting people across the nation to demand justice, accountability, and systemic reform. Social media platforms played a pivotal role in amplifying public sentiment and shaping discourse during this historic mass uprising. In this study, we present a hybrid transformer-based sentiment analysis framework to decode public opinion expressed in social media comments during and after the revolution. We used a brand new dataset of 4,200 Bangla comments collected from social media. The framework employs advanced transformer-based feature extraction techniques, including BanglaBERT, mBERT, XLM-RoBERTa, and the proposed hybrid XMB-BERT, to capture nuanced patterns in textual data. Principle Component Analysis (PCA) were utilized for dimensionality reduction to enhance computational efficiency. We explored eleven traditional and advanced machine learning classifiers for identifying sentiments. The proposed hybrid XMB-BERT with the voting classifier achieved an exceptional accuracy of 83.7% and outperform other model classifier combinations. This study underscores the potential of machine learning techniques to analyze social sentiment in low-resource languages like Bangla.