Data mining emotion in social network communication: Gender differences in MySpace
Journal of the American Society for Information Science and Technology
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
Lexical normalisation of short text messages: makn sens a #twitter
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Contextual bearing on linguistic variation in social media
LSM '11 Proceedings of the Workshop on Languages in Social Media
Modeling topic specific credibility on twitter
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Reading the correct history?: modeling temporal intention in resource sharing
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
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Human moods continuously change over time. Tracking moods can provide important information about psychological and health behavior of an individual. Also, history of mood information can be used to predict the future moods of individuals. In this paper, we try to predict the mood transition of a Twitter user by regression analysis on the tweets posted over twitter time line. Initially, user tweets are automatically labeled with mood labels from time 0 to t-1. It is then used to predict user mood transition information at time t. Experiments show that SVM regression attained less root-mean-square error compared to other regression approaches for mood transition prediction.