DeepBERT-XAI: a dual BERT approach with XAI for sentiment analysis of airline tweet data

Abstract

The rapid expansion of social media platforms, particularly Twitter, has transformed how businesses engage in customer sentiments and improve service quality. This study presents DeepBERT-XAI, a hybrid approach that integrates the powerful bidirectional encoder representations from transformers (BERT) architecture with explainable artificial intelligence (XAI) to perform sentiment analysis on 50,000 labeled airline tweets. This study addresses the interpretability of sentiment predictions, providing businesses with actionable insights into customer feedback. Using a dual BERT architecture, the model could effectively process and analyze the language of Twitter posts, accurate sentiment classifications and transparent explanations. The performance of DeepBERT-XAI was assessed using key metrics, and it achieve a training accuracy of 99.00%, validation accuracy of 98.50%, and test accuracy of 98.00%. In addition, it achieved an F1-score of 97.0%, recall of 96.80%, and precision of 97.90%. The significance of this study lies in its context-aware dual BERT fusion and domain-grounded explainability, which uniquely adapts to airline-specific feedback in real time. Unlike static domain-adapted models (AirBERT), DeepBERT-XAI dynamically weights general and domain-specific features via multi-head attention.