Next Article in Journal
Security Enhancement for Data Migration in the Cloud
Previous Article in Journal
Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems
Article Menu
Issue 2 (June) cover image

Export Article

Open AccessArticle
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

Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China
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]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Future Internet EISSN 1999-5903 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top