In this study, the emotion and tone of preservice teachers' reflections were analyzed using sentiment analysis with LLMs: GPT-4, Gemini, and BERT. We compared the results to understand how each tool categorizes and describes individual reflections and multiple reflections as a whole. This study aims to explore ways to bridge the gaps between qualitative, quantitative, and computational analyses of reflective practices in teacher education. This study finds that to effectively integrate LLM analysis into teacher education, developing an analysis method and result format that are both comprehensive and relevant for preservice teachers and teacher educators is crucial.
Table-Filling via Mean Teacher for Cross-domain Aspect Sentiment Triplet Extraction
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Hong Kong University of Science and Technology（香港科技大学）；Beijing Institute of Technology（北京理工大学）；North China Electric Power University（华北电力大学）；Beihang University（北京航空航天大学）；China Academy of Electronic and Information Technology（中国电子信息产业发展研究院）
C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis
School of Computer Science, South China Normal University（华南师范大学计算机科学学院）；School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子与信息工程学院）
Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development
Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA（电气与计算机工程系，亚利桑那大学，图森，亚利桑那州，美国）；Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, USA（系统与工业工程系，亚利桑那大学，图森，亚利桑那州，美国）；Department of Industrial Engineering, University of Sonora, Hermosillo, Mexico（工业工程系，索诺拉大学，赫尔莫索，墨西哥）
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models
Pingtan Research Institute of Xiamen University（厦门大学滨海研究院）；Imperial College London（伦敦帝国理工学院）；University of Sussex（苏塞克斯大学）；Purdue University（普渡大学）；University of Liverpool（利物浦大学）；JTB Technology Corp.（JTB技术公司）；Emory University（埃默里大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Kyoto University（京都大学）；AppCubic；Georgia Institute of Technology（佐治亚理工学院）；AI Agent Lab（AI代理实验室）
Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters?
University of Glasgow（格拉斯哥大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Artificial Intelligence, Shandong University（山东大学人工智能学院）
