BAN-ASTE: A unified neural framework for Bengali aspect sentiment triplet extraction

Triplet extraction-aspect, opinion, and sentiment detection for each product review is a valuable contribution to fine-grained sentiment analysis. Though this task is extensively studied for English and other large languages, triplet extraction for Bengali was never addressed by any prior work. In this paper, we introduce the neural system for Bengali aspect-opinion-sentiment triplet extraction leveraging the harmony between BanglaBERT embeddings and BiLSTM structures in a multi-stage pipeline. To enable this task, we present the BPR Corpus, a large manually annotated Bengali dataset with fine-grained triplet labels, filling a critical resource gap in low-resource language research. Our approach meticulously extracts aspect terms, extracts corresponding opinion words, and determines sentiment polarity for every pair, achieving F1 scores of more than 0.82 on all subtasks. Exhaustive experiments on genuine product reviews corroborate the generality and scalability of our model. This study establishes a new benchmark for sentiment analysis for Bengali and provides data and methodological foundations for under-resourced languages for future research.