Advancing Multimodal Teacher Sentiment Analysis:The Large-Scale T-MED Dataset & The Effective AAM-TSA Model

TL;DR

This paper introduces the first large-scale multimodal teacher sentiment dataset, T-MED, and proposes a novel AAM-TSA model that effectively captures teacher emotions by integrating multimodal data and instructional context.

Contribution

The paper presents a new large-scale teacher sentiment dataset and a novel asymmetric attention-based model for improved multimodal emotion analysis.

Findings

AAM-TSA outperforms existing methods in accuracy.

T-MED dataset includes 14,938 instances from diverse classrooms.

The model enhances interpretability and emotional classification precision.

Abstract

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.