A Knowledge-Enhanced Cross-Modal Transformer Network for Sentiment Analysis in Intelligent Interaction

With the rapid advancement of multimodal emotion recognition technology, sentiment analysis models that integrate heterogeneous information—such as facial expressions and vocal intonation—are driving human–computer interaction and affective computing toward multidimensional, objective, and highly accurate approaches. Conventional emotion recognition methods typically rely on a single-modal input and therefore struggle to capture complex semantic associations and deep emotional features, which in turn undermines the stability of recognition results. A knowledge-enhanced cross-modal Transformer network (KCTN) model was proposed for sentiment analysis, which incorporates a multimodal fusion module and a long-range affective integration module to achieve deep collaborative modeling across text, speech, and facial expression features. This framework substantially enhances the completeness and robustness of emotional semantic representations. Experimental results on the self-built EC-SFED multimodal dataset and the publicly available dataset CMU-MOSI demonstrate that KCTN surpasses several mainstream baseline models in both accuracy and macro-averaged F1 score, validating its superior performance in intelligent interaction and affective computing applications.