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Tracking the evolution of social emotions with topic models

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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.

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Notes

  1. http://news.sina.com.cn/.

  2. http://news.sina.com.cn/society/.

  3. http://emotiondata.sinaapp.com/.

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Acknowledgments

This research was partially supported by grants from the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), the National High Technology Research and Development Program of China (Grant No. 2014AA015203), the Fundamental Research Funds for the Central Universities of China (Grant No. WK2350000001), the Natural Science Foundation of China (Grant No. 61403358), NIH R21AA02395-01, and the Anhui Provincial Natural Science Foundation (Grant No. 1408085QF110).

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China

    Chen Zhu, Enhong Chen, Qi Liu & Tong Xu

  2. Big Data Lab, Baidu Research, Beijing, 100085, China

    Hengshu Zhu

  3. Department of Computer Science, The University of North Carolina at Charlotte, University City Blvd, Charlotte, NC, 28223-0001, USA

    Yong Ge

  4. Management Science and Information Systems Department, Rutgers Business School, Rutgers University, Newark, NJ, 07102, USA

    Hui Xiong

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  1. Chen Zhu
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  2. Hengshu Zhu
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  3. Yong Ge
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  4. Enhong Chen
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  5. Qi Liu
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  6. Tong Xu
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Correspondence to Enhong Chen.

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Zhu, C., Zhu, H., Ge, Y. et al. Tracking the evolution of social emotions with topic models. Knowl Inf Syst 47, 517–544 (2016). https://doi.org/10.1007/s10115-015-0865-0

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