Emotion and Intention Detection in a Large Language Model

Emmanuel Castro, Hiram Calvo, Olga Kolesnikova

Abstract: Large language models (LLMs) have recently shown remarkable capabilities in natural language processing. In this work, we investigate whether an advanced LLM can recognize user emotions and intentions from text, focusing on the open-source model DeepSeek. We evaluate zero-shot emotion classification and dialog act (intention) classification using two benchmark conversational datasets (IEMOCAP and MELD). We test the model under various prompting conditions, including those with and without conversational context, as well as with auxiliary information (dialog act labels or emotion labels). Our results show that DeepSeek achieves an accuracy of up to 63% in emotion recognition on MELD, utilizing context and dialog-act information. In the case of intention recognition, the model improved from 45% to 61% with the aid of context, but no further improvement was observed with the provision of emotional cues. Supporting the hypothesis that providing conversational context aids emotion and intention detection. However, conversely, adding emotion cues did not enhance intent classification, suggesting an asymmetric relationship. These findings highlight both the potential and limitations of current LLMs in understanding affective and intentional aspects of dialogue. For comparison, we also ran the same emotion and intention detection tasks on GPT-4 and Gemini-2.5. DeepSeek-r1 performed as well as Gemini-2.5 and better than GPT-4, confirming its place as a strong, competitive model in the field.

External IDs:doi:10.3390/math13233768