A Chinese Multi-label Affective Computing Dataset Based on Social Media Network Users
This addresses a gap for researchers in psychology, education, marketing, finance, and politics by providing a fine-grained Chinese dataset, though it is incremental as it builds on existing methods for data collection.
The study tackled the scarcity of Chinese affective computing datasets by creating a multi-label dataset from Weibo, integrating personality traits with six emotions and micro-emotions annotated with intensity levels, and validation across NLP models showed strong utility.
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.