Sentiment analysis and emotion detection are important research topics in natural language processing (NLP) and benefit many downstream tasks. With the widespread application of LLMs, researchers have started exploring the application of LLMs based on instruction-tuning in the field of sentiment analysis. However, these models only focus on single aspects of affective classification tasks (e.g. sentimental polarity or categorical emotions), and overlook the regression tasks (e.g. sentiment strength or emotion intensity), which leads to poor performance in downstream tasks. The main reason is the lack of comprehensive affective instruction tuning datasets and evaluation benchmarks, which cover various affective classification and regression tasks. Moreover, although emotional information is useful for downstream tasks, existing downstream datasets lack high-quality and comprehensive affective annotations. In this paper, we propose EmoLLMs, the first series of open-sourced instruction-following LLMs for comprehensive affective analysis based on fine-tuning various LLMs with instruction data, the first multi-task affective analysis instruction dataset (AAID) with 234K data samples based on various classification and regression tasks to support LLM instruction tuning, and a comprehensive affective evaluation benchmark (AEB) with 14 tasks from various sources and domains to test the generalization ability of LLMs. We propose a series of EmoLLMs by fine-tuning LLMs with AAID to solve various affective instruction tasks. We compare our model with a variety of LLMs on AEB, where our models outperform all other open-sourced LLMs, and surpass ChatGPT and GPT-4 in most tasks, which shows that the series of EmoLLMs achieve the ChatGPT-level and GPT-4-level generalization capabilities on affective analysis tasks, and demonstrates our models can be used as affective annotation tools.
Affective Multimodal Agents with Proactive Knowledge Grounding for Emotionally Aligned Marketing Dialogue
Hunan Police Academy, Department of Criminal Investigation（湖南警察学院犯罪侦查系）；Business College, California State University（加州州立大学长滩分校商学院）；Northwestern University（西北大学）；AI Agent Lab, Vokram Group（Vokram集团AI代理实验室）；Beijing University of Technology（北京理工大学）；Zheng Zhou University of Light Industry（郑州轻工业大学）
APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation
Sun Yat-Sen University（中山大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
iMiGUE-Speech: A Spontaneous Speech Dataset for Affective Analysis
Center for Machine Vision and Signal Analysis, University of Oulu, Finland（机器视觉与信号分析中心，奥卢大学，芬兰）；Center for Machine Vision（机器视觉中心）；Signal Analysis, University of Oulu, Finland（信号分析，奥卢大学，芬兰）
Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition
Fudan University（复旦大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Fujian University of Technology（福建工程学院）；Xiamen University（厦门大学）；Shanghai Center for Brain Science and Brain-Inspired Technology（上海脑科学与类脑研究中心）
