Knowledge and Information Systems

, Volume 47, Issue 3, pp 517–544

Tracking the evolution of social emotions with topic models

  • Chen Zhu
  • Hengshu Zhu
  • Yong Ge
  • Enhong Chen
  • Qi Liu
  • Tong Xu
  • Hui Xiong
Regular Paper

DOI: 10.1007/s10115-015-0865-0

Cite this article as:
Zhu, C., Zhu, H., Ge, Y. et al. Knowl Inf Syst (2016) 47: 517. doi:10.1007/s10115-015-0865-0
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Abstract

Many of today’s online news Web sites have enabled users to specify different types of emotions (e.g., angry or shocked) they have after reading news. Compared with traditional user feedbacks such as comments and ratings, these specific emotion annotations are more accurate for expressing users’ personal emotions. In this paper, we propose to exploit these users’ emotion annotations for online news in order to track the evolution of emotions, which plays an important role in various online services. A critical challenge is how to model emotions with respect to time spans. To this end, we propose a time-aware topic modeling perspective for solving this problem. Specifically, we first develop two models named emotion-Topic over Time (eToT) and mixed emotion-Topic over Time (meToT), in which the topics of news are represented as a beta distribution over time and a multinomial distribution over emotions. While they can uncover the latent relationship among news, emotion and time directly, they cannot capture the evolution of topics. Therefore, we further develop another model named emotion-based Dynamic Topic Model (eDTM), where we explore the state space model for tracking the evolution of topics. In addition, we demonstrate that all of proposed models could enable several potential applications, such as emotion prediction, emotion-based news recommendations, and emotion anomaly detections. Finally, we validate the proposed models with extensive experiments with a real-world data set.

Keywords

Social emotions Topic models Sentiment analysis 

Funding information

Funder NameGrant NumberFunding Note
National Science Foundation for Distinguished Young Scholars of China
  • 61325010
National High Technology Research and Development Program of China
  • 2014AA015203
Fundamental Research Funds for the Central Universities of China
  • WK2350000001
Natural Science Foundation of China
  • 61403358
Anhui Provincial Natural Science Foundation
  • 1408085QF110
National Institutes of Health (US)
  • R21AA02395-01

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Chen Zhu
    • 1
  • Hengshu Zhu
    • 2
  • Yong Ge
    • 3
  • Enhong Chen
    • 1
  • Qi Liu
    • 1
  • Tong Xu
    • 1
  • Hui Xiong
    • 4
  1. 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Big Data LabBaidu ResearchBeijingChina
  3. 3.Department of Computer ScienceThe University of North Carolina at CharlotteCharlotteUSA
  4. 4.Management Science and Information Systems Department, Rutgers Business SchoolRutgers UniversityNewarkUSA

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