When a Nation Speaks: Machine Learning and NLP in People's Sentiment Analysis During Bangladesh's 2024 Mass Uprising

TL;DR

This study pioneers sentiment analysis in Bangla during Bangladesh's 2024 mass uprising, using NLP techniques on a novel dataset to understand public emotions and the impact of political events.

Contribution

It introduces a new dataset of Bangla news headlines during a crisis and compares NLP models, highlighting the effectiveness of language-specific transformers in sentiment analysis.

Findings

Multilingual transformers achieved 67-71% accuracy.

Traditional machine learning methods reached 70% accuracy.

Themes like political corruption and protests were identified.

Abstract

Sentiment analysis, an emerging research area within natural language processing (NLP), has primarily been explored in contexts like elections and social media trends, but there remains a significant gap in understanding emotional dynamics during civil unrest, particularly in the Bangla language. Our study pioneers sentiment analysis in Bangla during a national crisis by examining public emotions amid Bangladesh's 2024 mass uprising. We curated a unique dataset of 2,028 annotated news headlines from major Facebook news portals, classifying them into Outrage, Hope, and Despair. Through Latent Dirichlet Allocation (LDA), we identified prevalent themes like political corruption and public protests, and analyzed how events such as internet blackouts shaped sentiment patterns. It outperformed multilingual transformers (mBERT: 67%, XLM-RoBERTa: 71%) and traditional machine learning methods (SVM and Logistic Regression: both 70%). These results highlight the effectiveness of language-specific models and offer valuable insights into public sentiment during political turmoil.