EmoBERTa-CNN: Hybrid Deep Learning Approach Capturing Global Semantics and Local Features for Enhanced Emotion Recognition in Conversational Settings

Abstract

Emotion recognition in conversations is a key task in natural language processing that enhances the quality of human-computer interactions. Although existing deep learning and Transformer-based pretrained language models have shown remarkably enhanced performances, both approaches have inherent limitations. Deep learning models often fail to capture the global semantic context, whereas Transformer-based pretrained language models can overlook subtle, local emotional cues. To overcome these challenges, we developed EmoBERTa-CNN, a hybrid framework that combines EmoBERTa's ability to capture global semantics with the capability of convolutional neural networks (CNNs) to extract local emotional features. Experiments on the SemEval-2019 Task 3 and Multimodal EmotionLines Dataset (MELD) demonstrated that the proposed EmoBERTa-CNN model achieved F1-scores of 96.0% and 79.45%, respectively, significantly outperforming existing methods and confirming its effectiveness for emotion recognition in conversations.