@inproceedings{duan-etal-2026-coarse,
    title = "From Coarse to Fine: A Multi-Granularity Multimodal Framework for Teacher Sentiment Analysis",
    author = "Duan, Zhiyi  and
      Wang, Xiangren  and
      Guan, Jiangshan  and
      Jia, Bing  and
      Xing, Qianli",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.431/",
    pages = "8861--8876",
    ISBN = "979-8-89176-395-1",
    abstract = "Teacher sentiment analysis is pivotal for understanding instructional dynamics, yet it remains challenging because classroom expressions are professionally regulated performances rather than spontaneous outbursts. However, existing approaches typically treat sentiment as a static, monolithic label, failing to capture this structured heterogeneity. To effectively model this complexity, we decompose teacher sentiment into three granularities: coarse-level performativity, medium-level intra-class heterogeneity, and fine-level cross-modal complementarity. Guided by this perspective, we propose CF-TSA, a coarse-to-fine multimodal framework. Specifically, we employ CLS-guided cross-modal attention to recover effective signals from regulated displays (coarse-level), thresholded substyle discovery to identify latent pedagogical styles (medium-level), and substyle-aware contrastive learning to align dynamic multimodal cue compositions (fine-level). Experiments on T-MED and CMU-MOSEI demonstrate that CF-TSA consistently outperforms state-of-the-art baselines, validating the effectiveness of the coarse-to-fine perspective and the hierarchical modeling."
}