Abstract
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.
Highlights
In the psychological field of emotion science, emotions play a crucial role in people’s decision-making [1]
The Profile of Mood State (POMS) is a widely-used scale for the measurement of mood state and the abbreviated POMS used in this study consists of seven mood scales, measuring different aspects of psychological health of individuals [12]
The online information was used to calculate the psychological variables and this cyber psychometrics method focuses on making linkages from online behavior to psychological traits
Summary
In the psychological field of emotion science, emotions play a crucial role in people’s decision-making [1]. This study considered a stable and continuous emotional variable, mood state, trying to build a model to identify the mood state of social network users automatically. This study proposed a multitask model to automatically calculate the long-term, continuous mood state, and a natural language processing technology was used to build the mood state lexicon in order to extract emotional information. The automated information extraction method proposed by this study could be used to explore or track the preferences of social network users and provide reference information for political decisions as a growing number of citizens choose to express opinions and sentiments online. This automatic calculation method may be widely applied in predicting other psychological variables or building pictures of users considering the rich information generated by social network users [20].
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