Human emotions are often not expressed directly, but regulated according to internal processes and social display rules. For affective computing systems, an understanding of how users regulate their emotions can be highly useful, for example to provide feedback in job interview training, or in psychotherapeutic scenarios. However, at present no method to automatically classify different emotion regulation strategies in a cross-user scenario exists. At the same time, recent studies showed that instruction-tuned Large Language Models (LLMs) can reach impressive performance across a variety of affect recognition tasks such as categorical emotion recognition or sentiment analysis. While these results are promising, it remains unclear to what extent the representational power of LLMs can be utilized in the more subtle task of classifying users' internal emotion regulation strategy. To close this gap, we make use of the recently introduced \textsc{Deep} corpus for modeling the social display of the emotion shame, where each point in time is annotated with one of seven different emotion regulation classes. We fine-tune Llama2-7B as well as the recently introduced Gemma model using Low-rank Optimization on prompts generated from different sources of information on the \textsc{Deep} corpus. These include verbal and nonverbal behavior, person factors, as well as the results of an in-depth interview after the interaction. Our results show, that a fine-tuned Llama2-7B LLM is able to classify the utilized emotion regulation strategy with high accuracy (0.84) without needing access to data from post-interaction interviews. This represents a significant improvement over previous approaches based on Bayesian Networks and highlights the importance of modeling verbal behavior in emotion regulation.
EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）；School of Software, BNRist, Tsinghua University（清华大学软件学院北京信息科学与技术国家研究中心）
Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation
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）；Beihang University（北航）；National University of Defense Technology（国防科技大学）；Shantou University（汕头大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Singapore University of Technology and Design（新加坡科技设计大学）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）；Adobe Research（奥多比研究院）；Instituto Politécnico Nacional（墨西哥国立理工学院）；University of Michigan（密歇根大学）
Linguistic Distancing on Social Media: Indicators of Emotion Regulation Across Age Groups
Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；Department of Computing Science, University of Alberta（阿尔伯塔大学计算机科学系）；National Research Council Canada（加拿大国家研究委员会）；Department of Psychology, University of Alberta（阿尔伯塔大学心理学系）
Decoding Emotions in Abstract Art: Cognitive Plausibility of CLIP in Recognizing Color-Emotion Associations
Institute for Interdisciplinary Studies, University of Amsterdam（阿姆斯特丹大学跨学科研究所）；Department of Information and Computing Sciences, Utrecht University（乌得勒支大学信息与计算科学系）
Emotion Recognition for Vietnamese Social Media Text
University of Information Technology, VNU-HCM（胡志明市信息技术大学（VNU-HCM））；Vietnam National University, Ho Chi Minh City（越南胡志明市国家大学）；University of Social Sciences and Humanities, VNU-HCM（胡志明市社会科学与人文大学（VNU-HCM））；Multimedia Communications Laboratory, University of Information Technology, VNU-HCM（胡志明市信息技术大学多媒体通信实验室（VNU-HCM））
Emotion Correlation Mining Through Deep Learning Models on Natural Language Text
Shanghai Institute for Advanced Communication and Data Science, Shanghai University（上海大学上海先进通信与数据科学研究院）；Center for Data Science and Big Data Analytics, Oakland University（奥克兰大学数据科学与大数据分析中心）
