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Predicting Happiness State Based on Emotion Representative Mining in Online Social Networks

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Advances in Knowledge Discovery and Data Mining (PAKDD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10234))

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Abstract

Online social networks (OSNs) have become a major platform for people to obtain information and to interact with their friends. People tend to post their thoughts and activities online and share their emotions with friends, which provides a good opportunity to study the role of online social networks in happiness spreading and mutual influence among the users. In this paper, we propose a framework to study the influence of happiness in OSNs. We first quantify the happiness states of users by analyzing their daily posting texts, and then conduct the statistical analysis to show that users’ happiness states are influenced by their social network neighbors. Since the influence of each individual is unequal, we develop a regression model and a greedy algorithm to detect the high influence users known as emotion representatives. By using a small number of detected emotion representatives as features to train prediction models, we show that it achieves good performance in predicting the happiness states of the whole online social network users.

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61672278, 61373128, 61321491), the EU FP7 IRSES MobileCloud Project (Grant No. 612212), the Collaborative Innovation Center of Novel Software Technology and Industrialization, and the project from State Grid Corporation of China (Research on Key Clustering Technology for Hyperscale Power Grid Control System).

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Author information

Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Xiao Zhang, Wenzhong Li, Cam-Tu Nguyen, Xiaoliang Wang & Sanglu Lu

  2. Sino-German Institutes of Social Computing, Nanjing University, Nanjing, China

    Wenzhong Li, Xiaoliang Wang & Sanglu Lu

  3. Institute of Computer Science, University of Gottingen, Gottingen, Germany

    Hong Huang

  4. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Xu Chen

Authors
  1. Xiao Zhang
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  2. Wenzhong Li
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  3. Hong Huang
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  4. Cam-Tu Nguyen
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  5. Xu Chen
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  6. Xiaoliang Wang
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  7. Sanglu Lu
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Corresponding author

Correspondence to Wenzhong Li .

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Zhang, X. et al. (2017). Predicting Happiness State Based on Emotion Representative Mining in Online Social Networks. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_30

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  • DOI: https://doi.org/10.1007/978-3-319-57454-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57453-0

  • Online ISBN: 978-3-319-57454-7

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