Graph-based interpretable dialogue sentiment analysis: A HybridBERT-LSTM framework with semantic interaction explainer
Ercan Atagün, Günay Temür, Serdar Biroğul · Computer Standards & Interfaces · 2025
Conversational sentiment analysis in natural language processing faces substantial challenges due to intricate contextual semantics and temporal dependencies within multi-turn dialogues. We present a novel HybridBERT-LSTM architecture that integrates BERT’s contextualized embeddings with LSTM’s sequential processing capabilities to enhance sentiment classification performance in dialogue scenarios. Our framework employs a dual-pooling mechanism to capture local semantic features and global discourse dependencies, addressing limitations of conventional approaches. Comprehensive evaluation on IMDb benchmark and real-world dialogue datasets demonstrates that HybridBERT-LSTM consistently improves over standalone models (LSTM, BERT, CNN, SVM) across accuracy, precision, recall, and F1-score metrics. The architecture effectively exploits pre-trained contextual representations through bidirectional LSTM layers for temporal discourse modeling. We introduce WordContextGraphExplainer, a graph-theoretic interpretability framework addressing conventional explanation method limitations. Unlike LIME’s linear additivity assumptions treating features independently, our approach utilizes perturbation-based analysis to model non-linear semantic interactions. The framework generates semantic interaction graphs with nodes representing word contributions and edges encoding inter-word dependencies, visualizing contextual sentiment propagation patterns. Empirical analysis reveals LIME’s inadequacies in capturing temporal discourse dependencies and collaborative semantic interactions crucial for dialogue sentiment understanding. WordContextGraphExplainer explicitly models semantic interdependencies, negation scope, and temporal flow across conversational turns, enabling comprehensive understanding of both word-level contributions and contextual interaction influences on decision-making processes. This integrated framework establishes a new paradigm for interpretable dialogue sentiment analysis, advancing trustworthy AI through high-performance classification coupled with comprehensive explainability. • HybridBERT-LSTM outperforms ML models on IMDb and dialogue datasets across metrics. • WordContextGraphExplainer maps word relations to explain non-linear semantics.