构建首个中文多标签情感计算数据集，融合用户性格与细粒度情绪。
A Chinese Multi-label Affective Computing Dataset Based on Social Media Network Users
- 从微博采集1.1万用户56万条帖子，标注MBTI性格与六类情绪强度。
- 实现同一用户多情绪、微表情及强度的联合标注，填补中文数据空白。
- 适用于心理、教育、营销等领域的智能情感分析研究。
情绪与人格是理解人类心理状态的核心要素。情绪反映个体主观体验，人格则揭示相对稳定的行为与认知模式。现有情感计算数据集通常分别标注情绪与人格特质，缺乏对微表情及情绪强度的精细标注，且在单标签与多标签分类中均存在不足。中文情绪数据集极为稀缺，而涵盖中文用户人格特征的数据集更是凤毛麟角。为弥补上述缺口，本研究从主流社交媒体平台微博收集数据，从超5万名用户中筛选出11,338名有效用户，其具备多样化的MBTI人格标签，并获取了共计566,900条用户帖子及其对应的MBTI标签。基于EQN方法，构建了一个多标签中文情感计算数据集，整合同一用户的性格特征与六类情绪及微表情，每类均标注情绪强度。多种自然语言处理分类模型的验证结果表明该数据集具有强实用性。该数据集旨在推动复杂人类情感的机器识别，为心理学、教育、营销、金融及政治等领域研究提供数据支持。
原文摘要 · Abstract (English)
Emotion and personality are central elements in understanding human psychological states. Emotions reflect an individual subjective experiences, while personality reveals relatively stable behavioral and cognitive patterns. Existing affective computing datasets often annotate emotion and personality traits separately, lacking fine-grained labeling of micro-emotions and emotion intensity in both single-label and multi-label classifications. Chinese emotion datasets are extremely scarce, and datasets capturing Chinese user personality traits are even more limited. To address these gaps, this study collected data from the major social media platform Weibo, screening 11,338 valid users from over 50,000 individuals with diverse MBTI personality labels and acquiring 566,900 posts along with the user MBTI personality tags. Using the EQN method, we compiled a multi-label Chinese affective computing dataset that integrates the same user's personality traits with six emotions and micro-emotions, each annotated with intensity levels. Validation results across multiple NLP classification models demonstrate the dataset strong utility. This dataset is designed to advance machine recognition of complex human emotions and provide data support for research in psychology, education, marketing, finance, and politics.
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