IEEE J Biomed Health Inform. 2015 Jul;19(4):1246-52. doi: 10.1109/JBHI.2015.2403839. Epub 2015 Feb 13.
Research data on predisposition to mental health problems, and the fluctuations and regulation of emotions, thoughts, and behaviors are traditionally collected through surveys, which cannot provide a real-time insight into the emotional state of individuals or communities. Large datasets such as World Health Organization (WHO) statistics are collected less than once per year, whereas social network platforms, such as Twitter, offer the opportunity for real-time analysis of expressed mood. Such patterns are valuable to the mental health research community, to help understand the periods and locations of greatest demand and unmet need. We describe the "We Feel" system for analyzing global and regional variations in emotional expression, and report the results of validation against known patterns of variation in mood. 2.73 ×10(9) emotional tweets were collected over a 12-week period, and automatically annotated for emotion, geographic location, and gender. Principal component analysis (PCA) of the data illustrated a dominant in-phase pattern across all emotions, modulated by antiphase patterns for "positive" and "negative" emotions. The first three principal components accounted for over 90% of the variation in the data. PCA was also used to remove the dominant diurnal and weekly variations allowing identification of significant events within the data, with z-scores showing expression of emotions over 80 standard deviations from the mean. We also correlate emotional expression with WHO data at a national level and although no correlations were observed for the burden of depression, the burden of anxiety and suicide rates appeared to correlate with expression of particular emotions.
研究心理健康问题倾向、情绪、思想和行为波动及调节的相关数据传统上是通过调查收集的，但调查无法实时洞察个体或群体的情绪状态。世界卫生组织（WHO）等大型数据集的收集频率不到每年一次，而 Twitter 等社交网络平台则提供了实时分析表达情绪的机会。这些模式对于心理健康研究界很有价值，可以帮助了解需求最大和未满足需求的时期和地点。我们描述了用于分析全球和区域情绪表达变化的“我们的感受”（We Feel）系统，并报告了针对已知情绪变化模式进行验证的结果。在 12 周的时间里，我们收集了 27.3 亿条情感推文，并对其进行了自动标注，以确定情绪、地理位置和性别。数据的主成分分析（PCA）表明，所有情绪都呈现出主导的同相模式，而“积极”和“消极”情绪则呈现出相反的模式。前三个主成分占数据变化的 90%以上。PCA 还用于去除主导的昼夜和每周变化，从而可以识别数据中的重要事件，其 z 分数显示情绪表达超出平均值 80 个标准差。我们还将情绪表达与国家层面的 WHO 数据进行了相关性分析，尽管抑郁负担没有相关性，但焦虑负担和自杀率似乎与特定情绪的表达相关。
