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Prevailing Trends Detection of Public Opinions Based on Tianya Forum

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Intelligent Data Engineering and Automated Learning – IDEAL 2013 (IDEAL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

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Abstract

How to catch both the central topics and the trend of topics over the on-line discussions are not only of theoretical importance for scientific research, but also of practical importance for social management in current China. In social management perspective, making intervention toward crisis timely and precisely depends on the right image or perception of public opinions toward the crisis. In our research, topic modeling is applied to explore the changing topics of new posts collected from Tianya Zatan Board of Tianya Club. Those online data reflect the community opinions toward social problems.

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References

  1. Zheng, R., Shi, K., Li, S.: The Influence Factors and Mechanism of Societal Risk Perception. In: Zhou, J. (ed.) Complex 2009, Part II. LNICST, vol. 5, pp. 2266–2275. Springer (2009)

    Google Scholar 

  2. Zhang, Z.D., Tang, X.J.: A Preliminary Study of Web Mining for Tianya Forum. In: Proceedings of the 11th Youth Conference of Systems Science and Management Science and 7th Conference of Logistic Systems Technology, pp. 199–204. Wuhan University of Science and Engineering Press, Wuhan (2011) (in Chinese)

    Google Scholar 

  3. Tang, X.J., Luo, B.: Understanding College Students’ Thought Toward Social Events by Qualitative Meta-Synthesis Technologies. International Journal of Organizational and Collective Intelligence 2(4), 15–30 (2011)

    Article  MathSciNet  Google Scholar 

  4. Tang, X.J.: Qualitative Meta-synthesis Techniques for Analysis of Public Opinions for in-depth Study. In: Zhou, J. (ed.) Complex 2009, Part II. LNICST, vol. 5, pp. 2338–2353. Springer (2009)

    Google Scholar 

  5. Blei, D.M., Lafferty, J.D.: A correlated topic model of Science. J. Annals of Applied Statistics 1, 17–35 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. J. Journal of Machine Learning Research 3, 993–1022 (2003)

    MATH  Google Scholar 

  7. Hall, D., Jurafsky, D., Manning, C.D.: Studying the history of ideas using topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 363–371. Association for Computational Linguistics (2008)

    Google Scholar 

  8. Blei, D.M., Lafferty, J.D.: Dynamic Topic Models. In: Proceedings of the 23rd International Conference on Machine Learning (2006)

    Google Scholar 

  9. Alsumait, L., Barbara, D., Domeniconi, C.: On-line LDA: Adaptive topic models for mining text streams with applications to topic detection and tracking. In: Eighth IEEE International Conference on ICDM 2008, pp. 3–12. IEEE (2008)

    Google Scholar 

  10. Wang, X., McCallum, A.: Topics over Time: A Non-Markov Continuous-Time Model of Topical Trends. In: KDD 2006, USA (2006)

    Google Scholar 

  11. Wang, C., Blei, D., Heckerman, D.: Continuous time dynamic topic models. In: Uncertainty in Artificial Intelligence, UAI (2008)

    Google Scholar 

  12. Meng, C., Zhang, M., Guo, W.: Evolution of Movie Topics Over Time (2012), http://cs229.stanford.edu/projects2012.html

  13. Wu, D., Tang, X.J.: Preliminary analysis of Baidu hot words. In: Proceedings of the 11th Youth Conference of Systems Science and Management Science, pp. 478–483. Wuhan University of Science and Engineering Press, Wuhan (2011) (in Chinese)

    Google Scholar 

  14. Cui, L.J., He, H., Liu, W.: Research on Hot Issues and Evolutionary Trends in Network Forums. International Journal of u- and e- Service, Science and Technology 6(2), 89–97 (2013)

    Google Scholar 

  15. Chen, X., Li, J., Li, S., Wang, Y.: Hierarchical Activeness State Evaluation Model for BBS Network Community. In: 7th International ICST Conference on Communications and Networking in China (CHINACOM), pp. 206–211. IEEE (2012)

    Google Scholar 

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Cao, L.N., Tang, X.J. (2013). Prevailing Trends Detection of Public Opinions Based on Tianya Forum. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_23

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  • DOI: https://doi.org/10.1007/978-3-642-41278-3_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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