基于生活日志的情绪识别

Emotion Recognition Based on Life Log Data

抑郁症日益成为影响现代人生活幸福程度的重要因素。实时有效地识别用户情绪的方法对于抑郁症潜在患者的发现和治疗十分有意义。用户情绪的状态及变化会体现在其生活日志数据上。该文从使用可穿戴设备收集的用户生活日志数据出发,对数据进行了特征方面的分析;进一步地,使用以回归树为弱学习器的集成学习模型,设计了使用全部数据、仅用户自身数据以及仅他人数据进行训练的三组实验构成的对比实验框架,以比较使用不同用户数据对识别结果的影响。实验结果表明,基于生活日志数据的集成学习模型可以有效地识别用户的情绪状态。同时,基于实验结果提出了用户认知不一致的猜想,对于心理学上的抑郁分析也有一定的启发作用。该工作是目前所知第一个利用用户生活日志信息进行情绪识别以及抑郁症患者分析的工作,为后续进一步扩大实验规模和改良实验设计提供了思路。

Depression is increasingly becoming an important factor affecting the happiness of modern people. The real time and effective identification of emotions is of great significance for the discovery and treatment of potential patients with depression. This paper analyzes the characteristics of the user’s life log data collected by wearable devices. Further, in this paper, we apply an ensemble learning model with regression tree as weak learner on three groups of experiments designed to use all data, only user’s own data and only others data for training, respectively. The experimental results show that the ensemble learning model based on life log data can effectively identify the emotional state of users. Meanwhile, the experimental results indicate a conjecture that users’ cognition is inconsistent, serving as a potential inspiration for psychological depression analysis as well.