MoodCast: Emotion prediction via dynamic continuous factor graph model

Yuan Zhang, Jie Tang, Jimeng Sun, Yiran Chen, Jinghai Rao

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

Abstract

Human emotion is one important underlying force affecting and affected by the dynamics of social networks. An interesting question is "can we predict a person's mood based on his historic emotion log and his social network?". In this paper, we propose a MoodCast method based on a dynamic continuous factor graph model for modeling and predicting users' emotions in a social network. MoodCast incorporates users' dynamic status information (e.g., locations, activities, and attributes) and social influence from users' friends into a unified model. Based on the historical information (e.g., network structure and users' status from time 0 to t-1), MoodCast learns a discriminative model for predicting users' emotion status at time t. To the best of our knowledge, this work takes the first step in designing a principled model for emotion prediction in social networks. Our experimental results on both real social network and virtual web-based network show that we can accurately predict emotion status of more than 62% of users and 8+% improvement than the baseline methods.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining, ICDM 2010
Pages1193-1198
Number of pages6
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event10th IEEE International Conference on Data Mining, ICDM 2010 - Sydney, NSW, Australia
Duration: Dec 14 2010Dec 17 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other10th IEEE International Conference on Data Mining, ICDM 2010
CountryAustralia
CitySydney, NSW
Period12/14/1012/17/10

ASJC Scopus subject areas

  • Engineering(all)

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