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A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics

机译：一种识别基于网络心理学学的社会网络用户情绪状态的方法

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摘要

Analyzing people’s opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for identifying the psychological characteristics of social network users. However, most studies focus on identifying the sentiments carried in the micro-blogging text and there is no ideal calculation method for users’ real emotional states. In this study, the Profile of Mood State (POMS) is used to characterize users’ real mood states and a regression model is built based on cyber psychometrics and a multitask method. Features of users’ online behavior are selected through structured statistics and unstructured text. Results of the correlation analysis of different features demonstrate that users’ real mood states are not only characterized by the messages expressed through texts, but also correlate with statistical features of online behavior. The sentiment-related features in different timespans indicate different correlations with the real mood state. The comparison among various regression algorithms suggests that the multitask learning method outperforms other algorithms in root-mean-square error and error ratio. Therefore, this cyber psychometrics method based on multitask learning that integrates structural features and temporal emotional information could effectively obtain users’ real mood states and could be applied in further psychological measurements and predictions.
机译：根据用户生成的内容（UGC）分析人们的意见，态度，情感和情感是可行的，可以识别社交网络用户的心理特征。然而，大多数研究侧重于识别微博文本中携带的情绪，并且对用户的真正情绪状态没有理想的计算方法。在本研究中，情绪状态（POMS）的简档用于表征用户的真实情绪状态，基于网络精神测量测量学和多任务方法构建回归模型。通过结构化统计和非结构化文本选择用户在线行为的功能。不同特征的相关分析结果表明，用户的真实情绪状态不仅是通过文本表达的消息的特征，而且与在线行为的统计特征相关。不同时间表中的情绪相关特征表示与真实情绪状态不同的相关性。各种回归算法之间的比较表明，多任务学习方法以根均方误差和误差比越优于其他算法。因此，这种基于多任务学习的网络精神测量方法，可以有效地获得用户的真实情绪状态，并且可以应用于进一步的心理测量和预测。

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