THE FUTURE OF BANGLA SENTIMENT ANALYSIS: ADVANCEMENTS, CHALLENGES, AND OPPORTUNITIES FOR PRACTICAL AND RESEARCH INNOVATION

Abstract

Bangla sentiment analysis has advanced significantly, transitioning from rule-based models and lexicons to deep learning and transformer-based architectures. Despite these developments, the field still faces critical challenges, including limited labeled data, complex morphology, code-mixed language, and dialectal variation. Although recent models and datasets have improved accuracy, key issues remain such as narrow domain coverage, underexplored aspect-based and emotion classification, and potential ethical concerns related to bias and fairness. This paper critically examines current approaches, including deep neural and cross-lingual models, and highlights new frontiers like multimodal sentiment analysis and real-time inference. It also outlines strategic directions for future research, focusing on zero-shot learning, dialogue-based sentiment detection, and fairness-aware frameworks. The study aims to provide a roadmap for making Bangla sentiment analysis both technologically robust and socially responsible.