Tang, J., Zhang, Y., Sub, J., Rao, J., Yu, W., Chen, Y. and Fong, A.C.M. (2012) Quantitative study of individual emotional states in social networks. IEEE Transactions on Affective Computing, 3(2), pp. 132-144. (doi: 10.1109/T-AFFC.2011.23)
Full text not currently available from Enlighten.
Abstract
Marketing strategies without emotion will not work. Emotion stimulates the mind 3,000 times quicker than rational thought. Such emotion invokes either a positive or a negative response and physical expressions. Understanding the underlying dynamics of users' emotions can efficiently help companies formulate marketing strategies and support after-sale services. While prior work has focused mainly on qualitative aspects, in this paper we present our research on quantitative analysis of how an individual's emotional state can be inferred from her historic emotion log and how this person's emotional state influences (or is influenced by) her friends in the social network. We statistically study the dynamics of individual's emotions and discover several interesting as well as important patterns. Based on this discovery, we propose an approach referred to as MoodCast to learn to infer individuals' emotional states. In both mobile-based social network and online virtual network, we verify the effectiveness of our proposed approach.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Fong, Dr Alvis Cheuk Min |
| Authors: | Tang, J., Zhang, Y., Sub, J., Rao, J., Yu, W., Chen, Y., and Fong, A.C.M. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| College/School: | College of Science and Engineering > School of Computing Science |
| Journal Name: | IEEE Transactions on Affective Computing |
| ISSN: | 1949-3045 |
| ISSN (Online): | 1949-3045 |
University Staff: Request a correction | Enlighten Editors: Update this record
Altmetric
Altmetric