BERT-KAN: Enhancing bilingual sentiment analysis in bangladeshi E-commerce through fine-tuned large language models

Sentiment analysis of code-mixed reviews poses unique challenges due to linguistic variability and contex- tual ambiguity, particularly in multilingual e-commerce environments. In this paper, we introduce BERT- KAN, a novel hybrid architecture that enhances bilingual sentiment analysis in Bangladeshi e-commerce by integrating the deep contextual representations of Bidirectional Encoder Representations from Transform- ers(BERT) with a Kolmogorov-Arnold Network (KAN) layer. The KAN component employs a polynomial expansion to capture complex non-linear relationships within code-mixed Bengali-English text, while an innovative polynomial attention mechanism further refines feature extraction. Extensive ablation studies were conducted on two base models—bert-base-multilingual-uncased and BanglaBERT—using polynomial degrees of 2 and 3. Notably, the best configuration for bert-base-multilingual-uncased (employing KAN, polynomial attention, and feature fusion with polynomial degree 2) achieved a precision of 95.3 %, recall of 97.0 %, and an F1-score of 96.1 %. Comparable performance was observed for polynomial degree 3 (precision 96.2 %, recall 95.8 %, and F1-score 96.0 %), while cross-validation experiments yielded average accuracies exceeding 90 % across multiple folds. Detailed error analyses, supported by confusion matrices and sam- ple predictions, as well as discussions on computational requirements and deployment challenges, further validate the robustness of our approach.