构建首个大规模教师多模态情感数据集，提出高效情感分析模型。
Advancing Multimodal Teacher Sentiment Analysis:The Large-Scale T-MED Dataset & The Effective AAM-TSA Model
- 设计异构注意力机制融合多模态数据，精准捕捉教师情绪变化。
- 在1.5万条真实课堂数据上实现超越现有方法的准确率。
- 适合教育智能、情感计算研究者使用，推动教学评估智能化。
教师情绪状态在教育场景中至关重要，深刻影响教学效果、学生参与度与学习成效。然而，现有研究常因表演性特征及忽略教学信息对情绪表达的影响，难以准确捕捉教师情绪。本文系统构建了教师情感分析的数据库与模型：首先建立首个大规模教师多模态情感分析数据集T-MED，涵盖250间真实教室中11个学科（从小学到高等教育）的14,938条多模态数据，包含文本、音频、视频与教学内容信息；采用人机协同标注流程确保标签质量与效率。同时提出新颖的异构注意力多模态教师情感分析模型AAM-TSA，引入异构注意力机制与分层门控单元，实现跨模态特征差异化融合与精确分类。实验表明，AAM-TSA在T-MED数据集上显著优于现有最优方法，在准确率与可解释性方面表现优异。
原文摘要 · Abstract (English)
Teachers' emotional states are critical in educational scenarios, profoundly impacting teaching efficacy, student engagement, and learning achievements. However, existing studies often fail to accurately capture teachers' emotions due to the performative nature and overlook the critical impact of instructional information on emotional expression. In this paper, we systematically investigate teacher sentiment analysis by building both the dataset and the model accordingly. We construct the first large-scale teacher multimodal sentiment analysis dataset, T-MED. To ensure labeling accuracy and efficiency, we employ a human-machine collaborative labeling process. The T-MED dataset includes 14,938 instances of teacher emotional data from 250 real classrooms across 11 subjects ranging from K-12 to higher education, integrating multimodal text, audio, video, and instructional information. Furthermore, we propose a novel asymmetric attention-based multimodal teacher sentiment analysis model, AAM-TSA. AAM-TSA introduces an asymmetric attention mechanism and hierarchical gating unit to enable differentiated cross-modal feature fusion and precise emotional classification. Experimental results demonstrate that AAM-TSA significantly outperforms existing state-of-the-art methods in terms of accuracy and interpretability on the T-MED dataset.
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