EmoBang: Detecting Emotion From Bengali Texts

TL;DR

This paper introduces a new Bengali emotion dataset and proposes two deep learning models, achieving over 92% accuracy, thus advancing emotion detection in low-resource languages.

Contribution

It presents the first comprehensive Bengali emotion dataset and benchmarks multiple models, including hybrid neural networks and ensemble transformers, for emotion detection.

Findings

EmoBangHybrid achieves 92.86% accuracy.

EmoBangEnsemble achieves 93.69% accuracy.

The models outperform existing classical approaches.

Abstract

Emotion detection from text seeks to identify an individual's emotional or mental state - positive, negative, or neutral - based on linguistic cues. While significant progress has been made for English and other high-resource languages, Bengali remains underexplored despite being the world's fourth most spoken language. The lack of large, standardized datasets classifies Bengali as a low-resource language for emotion detection. Existing studies mainly employ classical machine learning models with traditional feature engineering, yielding limited performance. In this paper, we introduce a new Bengali emotion dataset annotated across eight emotion categories and propose two models for automatic emotion detection: (i) a hybrid Convolutional Recurrent Neural Network (CRNN) model (EmoBangHybrid) and (ii) an AdaBoost-Bidirectional Encoder Representations from Transformers (BERT) ensemble model (EmoBangEnsemble). Additionally, we evaluate six baseline models with five feature engineering techniques and assess zero-shot and few-shot large language models (LLMs) on the dataset. To the best of our knowledge, this is the first comprehensive benchmark for Bengali emotion detection. Experimental results show that EmoBangH and EmoBangE achieve accuracies of 92.86% and 93.69%, respectively, outperforming existing methods and establishing strong baselines for future research.