We present our shared task on text-based emotion detection, covering more than 30 languages from seven distinct language families. These languages are predominantly low-resource and spoken across various continents. The data instances are multi-labeled into six emotional classes, with additional datasets in 11 languages annotated for emotion intensity. Participants were asked to predict labels in three tracks: (a) emotion labels in monolingual settings, (b) emotion intensity scores, and (c) emotion labels in cross-lingual settings. The task attracted over 700 participants. We received final submissions from more than 200 teams and 93 system description papers. We report baseline results, as well as findings on the best-performing systems, the most common approaches, and the most effective methods across various tracks and languages. The datasets for this task are publicly available.

本研究针对基于文本的情感检测，涵盖来自七个不同语言家族的30多种低资源语言，填补了这一领域的空白。通过设置情感标签预测的多条赛道，研究提供了基线结果和最佳系统的表现，为多语言情感分析提供了重要参考。该研究为情感识别的方法和效果提供了关键见解，促进了跨语言情感检测的进展。

SemEval-2025任务11：弥合基于文本的情感检测的差距