Heimerdinger at S em E val-2025 Task 11: A Multi-Agent Framework for Perceived Emotion Detection in Multilingual Text (2025)

Abstract: This paper presents our system developed for the SemEval-2025 Task 11: Text-Based Emotion Detection (TBED) task, which aims to identify the emotions perceived by the majority of people from a speaker’s short text. We introduce a multi-agent framework for emotion recognition, comprising two key agents: the Emotion Perception Profiler, which identifies emotions in text, and the Intensity Perception Profiler, which assesses the intensity of those emotions. We model the task using both generative and discriminative approaches, leveraging BERT series and large-scale generative language models (LLMs). A multi-system collaboration mechanism is employed to further enhance the accuracy, stability, and robustness. Additionally, we incorporate cross-lingual knowledge transfer to improve performance in diverse linguistic scenarios. Our method demonstrates superior results in emotion detection and intensity prediction across multiple subtasks, highlighting its effectiveness, especially in language adaptability.

## introduction emotion perception is a complex and subtle process involving how individuals perceive, express, and interpret emotions @xcite @xcite . this paper focuses on the text-based emotion detection (tbed) task proposed in the semeval-2025 task 11 @xcite , which aims to determine the emotion that the majority of people perceive the speaker to be experiencing based on a sentence or a short text fragment uttered by the speaker (along with further assessment of the intensity of emotions). the tbed task concerns the emotion perceived by the majority of people rather than the speaker's ♡ contribute equal to this work. i was trying to concentrate on not falling, but i slipped and almost fell on my face. ## conclusion in this paper, we propose a multi-agent framework for the text-based emotion detection (tbed) task in the semeval-2025 task 11. our system, comprising the emotion perception profiler and intensity perception profiler, demonstrates superior accuracy, stability, and robustness across multiple subtasks. the cross-lingual knowledge transfer and in-context learning strategies effectively address challenges in low-resource languages, enhancing language adaptability. extensive experiments demonstrate the effectiveness of our approach without the need for costly pre-training. future work will explore further optimizations in diverse linguistic and emotional contexts.