The majority of contemporary dialogue systems neglect human emotions as they hardly feel and never react towards emotions, thus inhibiting human-like human-computer interactions. We present here a new approach to Emotion-Aware Semantic Understanding (EASU) that fully integrates affective knowledge and language representation. Our method employs a Hybrid Contextual Embedding (HCE) model that places traditional semantic embeddings from large language models atop a second affective embedding stream derived from the user's vocabulary, tone, and dialogue context. The hybrid presentation enables the identification of literal meaning and implicated affect. We trained and evaluated our models on the Emotion-Annotated Conversational Corpus (EACC-440), a 440 fine-grained, multi-turn conversation dataset with emotional annotations. The system was implemented in Python using Hugging Face Transformers and NLTK libraries, which were utilised for semantic modelling and preliminary text analysis, respectively. Our findings reveal that the EASU-augmented dialogue agent produces more empathetic, contextually appropriate responses, resulting in measurable improvements in user engagement and satisfaction scores.
Keywords
Dialogue SystemsEmotion RecognitionNatural Language UnderstandingContextual EmbeddingHuman-Computer Interaction.
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