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Future Internet 2017, 9(2), 22; doi:10.3390/fi9020022

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

1
Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China
2
School of Information Management, Central China Normal University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editor: Nicola Lettieri
Received: 19 April 2017 / Revised: 11 June 2017 / Accepted: 12 June 2017 / Published: 16 June 2017
View Full-Text   |   Download PDF [847 KB, uploaded 16 June 2017]   |  

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. View Full-Text
Keywords: mood states; cyber psychometrics; Profile of Mood State (POMS); sentiment analysis; microblog mood states; cyber psychometrics; Profile of Mood State (POMS); sentiment analysis; microblog
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Wang, W.; Li, Y.; Huang, Y.; Liu, H.; Zhang, T. A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics. Future Internet 2017, 9, 22.

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