Predicting users’ emotional intelligence with social networking data

Xiangyu Wei, Jin Li, Zhen Han, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Social networks have integrated into daily lives of most people in the way of interactions and of lifestyles. The users’ identity, relationships, or other characteristics can be explored from the social networking data, in order to provide more personalized services to the users. In this work, we focus on predicting the user’s emotional intelligence (EI) based on the social networking data. As an essential facet of users’ psychological characteristics, EI plays an important role on well-being, interpersonal relationships, and overall success in people’s life. Most existing work on predicting users’ emotional intelligence is based on questionnaires that may collect dishonest answers or unconscientious responses, thus leading in potentially inaccurate prediction results. In this work, we are motivated to propose an emotional intelligence prediction model based on the sentiment analysis of social networking data. The model is represented by four dimensions including self-awareness, self-regulation, self-motivation and social relationships. The EI of a user is then measured by the four numerical values or the sum of them. In the experiments, we predict the EIs of over a hundred thousand users based on one of the largest social networks of China, Weibo. The predicting results demonstrate the effectiveness of our model. The results show that the distribution of the four EI’s dimensions of users is roughly normal. The results also indicate that EI scores of females are generally higher than males’. This is consistent with previous findings.
Original languageEnglish (US)
Title of host publicationCommunications in Computer and Information Science
PublisherSpringer Singapore
Number of pages12
ISBN (Print)9789811507571
StatePublished - Oct 24 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The work reported in this paper was supported in part by the Natural Science Foundation of China, under Grant U1736114 and by the National Key R&D Program of China, under Grant 2017YFB0802805.


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