A chinese multi-label affective computing dataset integrating mbti personality traits and emotion intensity from social media posts

CMACD: Chinese Multi-label Affective Computing Dataset # 📌 Overview CMACD is a large-scale Chinese affective computing dataset that integrates personality traits (MBTI) with multi-label emotion annotations and intensity scores. The dataset is constructed from social media text and provides a resource for studying the relationship between personality and emotional expression in natural language. --- # 📊 Dataset Statistics Users : 11,336 Posts : 566,800 Posts per user : 50 Text length : 30–150 Chinese characters Personality labels : 16 MBTI types Emotion labels : 6 categories anger, fear, happiness, neutrality, sadness, surprise Annotation type : Multi-label with intensity scores (0–1) --- # 🧠 Annotation Method Emotion annotations are generated using the Extended Quantification Network (EQN) framework based on a BERT model. The model assigns continuous intensity scores to each emotion, enabling both macro- and micro-emotion analysis. For more details, please refer to our paper: *Expansion quantization network: A micro-emotion detection and annotation framework* [https://doi.org/10.1371/journal.pone.0333930](https://doi.org/10.1371/journal.pone.0333930) Feel free to cite our article: Zhou, J., Luo, S., & Chen, H. (2025). Expansion quantization network: A micro-emotion detection and annotation framework. PloS one, 20(11), e0333930. --- # 📁 Data Format The folder CMACD contains 16 subfolders. Each subfolder is named after a personality type and contains several CSV files, with each CSV file corresponding to 50 posts from a single user. The dataset is organized as structured text samples: | text | anger | fear | happiness | neutral | sadness | surprise | | ------------ | ----- | ---- | --------- | ------- | ------- | -------- | | example text | 0.01 | 0.00 | 0.85 | 0.10 | 0.00 | 0.04 | ⚠️ Note: No user identifiers are included No raw profile-level data are provided --- # 🔒 Data Access (Important) Due to privacy and ethical considerations, the dataset is not publicly released in raw form . Researchers may request access for academic purposes only by contacting the corresponding author. Access will be granted under the following conditions: Non-commercial use only No attempt to re-identify users Compliance with research ethics guidelines --- # ⚖️ Ethics Statement All data were collected from publicly accessible sources. All identifiable information has been removed or anonymized. The dataset is shared in a de-identified format and is intended strictly for academic research. --- # 🧪 Benchmark Results We provide baseline results using multiple models: ## Personality Classification (Accuracy) | Model | I/E | N/S | T/F | J/P | | ----- | ---- | ---- | ---- | ---- | | BERT | 0.76 | 0.78 | 0.79 | 0.73 | ## Emotion Classification | Model | E1_Acc | Ex_Acc | | ----- | ------ | ------ | | BERT | 0.92 | 0.88 | --- # 📈 Potential Applications Affective computing Personality-aware NLP Computational psychology Social media analysis Human-computer interaction --- # 📜 License This dataset is available for academic research only . Commercial use is strictly prohibited. --- # 📖 Citation If you use this dataset, please cite: Zhou, J., Luo, S., & Chen, H. (2025). Expansion quantization network: A micro-emotion detection and annotation framework. PloS one, 20(11), e0333930. # ⭐ Acknowledgements We thank the contributors and the research community for supporting open research in affective computing.# 📌 概述 CMACD是一款大规模中文情感计算数据集，整合了人格特质（MBTI）、多标签情感标注及强度评分。 本数据集采集自社交媒体文本，为研究自然语言中人格与情感表达的关联提供了支撑资源。 --- # 📊 数据集统计 用户数：11,336 帖子数：566,800 单用户平均帖子数：50 文本长度：30~150个中文字符 人格标签：16种MBTI类型 情感标签：6大类，分别为愤怒（anger）、恐惧（fear）、喜悦（happiness）、中性（neutrality）、悲伤（sadness）、惊讶（surprise） 标注类型：带强度评分的多标签标注（评分范围0~1） --- # 🧠 标注方法 情感标注基于BERT模型，通过扩展量化网络（Extended Quantification Network，EQN）框架生成。该模型可为每种情感分配连续的强度评分，支持宏观与微观情感分析。 如需了解更多细节，请参阅我们的论文：*《扩展量化网络：一种微观情感检测与标注框架》* [https://doi.org/10.1371/journal.pone.0333930](https://doi.org/10.1371/journal.pone.0333930) 欢迎引用本文：Zhou, J., Luo, S., & Chen, H. (2025). Expansion quantization network: A micro-emotion detection and annotation framework. PLOS ONE, 20(11), e0333930. --- # 📁 数据格式 CMACD文件夹包含16个子文件夹，每个子文件夹以一种人格类型命名，内部包含若干CSV文件，每个CSV文件对应单个用户的50条帖子。 数据集采用结构化文本样本格式，示例如下： | 文本内容 | anger | fear | happiness | neutral | sadness | surprise | | ------- | ----- | ---- | --------- | ------- | ------- | -------- | | 示例文本 | 0.01 | 0.00 | 0.85 | 0.10 | 0.00 | 0.04 | ⚠️ 注意： - 未包含用户标识符 - 未提供原始用户档案级数据 --- # 🔒 数据获取（重要） 出于隐私与伦理考量，本数据集未以原始形式公开发布。仅学术研究人员可通过联系通讯作者申请使用权限。 申请获批需满足以下条件： - 仅可用于非商业用途 - 不得尝试重新识别用户身份 - 遵守研究伦理准则 --- # ⚖️ 伦理声明 所有数据均采集自公开可访问的来源，所有可识别信息均已删除或匿名化处理。本数据集以去标识化形式共享，仅可用于学术研究。 --- # 🧪 基准测试结果 我们提供了多种模型的基准测试结果： ## 人格分类（准确率） | 模型 | I/E | N/S | T/F | J/P | | ---- | --- | --- | --- | --- | | BERT | 0.76 | 0.78 | 0.79 | 0.73 | ## 情感分类 | 模型 | E1_Acc | Ex_Acc | | ---- | ------ | ------ | | BERT | 0.92 | 0.88 | --- # 📈 潜在应用场景 - 情感计算 - 人格感知自然语言处理 - 计算心理学 - 社交媒体分析 - 人机交互 --- # 📜 许可协议 本数据集仅可用于学术研究，严格禁止商业使用。 --- # 📖 引用格式 若使用本数据集，请引用以下文献： Zhou, J., Luo, S., & Chen, H. (2025). Expansion quantization network: A micro-emotion detection and annotation framework. PLOS ONE, 20(11), e0333930. --- # ⭐ 致谢 感谢所有贡献者与研究社区对情感计算领域开放研究的支持。