Exploring large language models for semantic classification of arguments of emotion predicates

When extracting arguments from emotive predicates, there is a need for automatic semantic (thematic) classification of the extracted arguments. The aim of this study is to investigate the feasibility of using large-scale language models (LLM) for automatic argument classification, and analyze the relationship between certainty and the quality of LLM results. Using the topic "Medicine and Healthcare," it has been experimentally demonstrated that the Claude Sonnet 4.5 language model enables high-quality argument classification. Furthermore, integrating explicit certainty assessment into prompts yields a statistically significant signal for identifying potentially unreliable predictions. The results of this study can be applied to systems for analyzing the emotional attitudes of social media users toward given topics and events.