Analyzing the tone of Bangla financial news is challenging because labeled data are scarce, the language is morphologically rich, and economic discourse shifts over time. We address these hurdles with a three-part framework. First, SSABE a S emi- S upervised A daptive B oosting E nsemble iteratively refines pseudo-labels, adjusts model weights by recent performance, and applies sector-aware voting to distill reliable labels from limited data. Second, the T emporal S entiment C ontrastive M odule ( TSCM ) aligns yearly embedding prototypes via contrastive loss, keeping the classifier robust against vocabulary drift and shifting economic regimes. Third, Temporal-SHAP yields token-level attributions that reveal how term importance changes across years and industries, thereby making the system transparent to analysts. Evaluated on a 5-year (2018–2023) Bangla financial news corpus spanning eight sectors, our pipeline attains a macro-F 1 of 0.782 and 91.4 % explanation fidelity surpassing fine-tuned transformer and self-training baselines by 6 %–12 % absolute. Performance remains stable when labels are scarce, sectors are imbalanced, or economic shocks such as the inflation and currency decline of 2023 occur. Moreover, yearly sentiment scores and Temporal-SHAP attributions track inflation and exchange-rate trends, confirming real-world relevance. The proposed framework offers a scalable, interpretable solution for monitoring emerging-market news, supporting regulators, policymakers, and investors who rely on trustworthy Bangla-language insights.
Khandokar et al. (Wed,) studied this question.