Knowledge and Information Systems

, Volume 39, Issue 3, pp 667–702 | Cite as

Mood sensing from social media texts and its applications

  • Thin NguyenEmail author
  • Dinh Phung
  • Brett Adams
  • Svetha Venkatesh
Regular Paper


We present a large-scale mood analysis in social media texts. We organise the paper in three parts: (1) addressing the problem of feature selection and classification of mood in blogosphere, (2) we extract global mood patterns at different level of aggregation from a large-scale data set of approximately 18 millions documents (3) and finally, we extract mood trajectory for an egocentric user and study how it can be used to detect subtle emotion signals in a user-centric manner, supporting discovery of hyper-groups of communities based on sentiment information. For mood classification, two feature sets proposed in psychology are used, showing that these features are efficient, do not require a training phase and yield classification results comparable to state of the art, supervised feature selection schemes; on mood patterns, empirical results for mood organisation in the blogosphere are provided, analogous to the structure of human emotion proposed independently in the psychology literature; and on community structure discovery, sentiment-based approach can yield useful insights into community formation.


Mood sensing Mood classification Mood pattern  Hyper-community 


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Thin Nguyen
    • 1
    Email author
  • Dinh Phung
    • 1
  • Brett Adams
    • 2
  • Svetha Venkatesh
    • 1
  1. 1.School of Information TechnologyDeakin UniversityGeelong, VIC 3220Australia
  2. 2.Department of ComputingCurtin UniversityPerthAustralia

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