Using Support Vector Machine for Classification of Baidu Hot Word

  • Yang Hu
  • Xijin Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8041)


Support vector machine (SVM) provides embarkation for solving multi-classification problem toward Web content. In this paper, we firstly introduce the workflow of Support Vector Machine. And we utilize SVM to automatically identifying risk category of Baidu hot word. Thirdly, we report the results with some dicsussions. Finally, future research topics are given.


SVM text classification text extraction Baidu hot word 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yang, Y.M., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development Information Retrieval, pp. 42–49 (1999)Google Scholar
  2. 2.
    Tong, S., Koller, D.: Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research 2, 45–66 (2001)Google Scholar
  3. 3.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys (CSUR) 34, 1–47 (2002)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Tsai, C.H.: MMSEG: A word identification system for Mandarin Chinese text based on two variants of the maximum matching algorithm,
  5. 5.
    Yang, Y.M., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proceeding of the 14th International Learning Conference on Machine Learning, pp. 412–420. Morgan Kaufmann Publishers (1997)Google Scholar
  6. 6.
    Lan, M., Tan, C.L., Su, J., Lu, Y.: Supervised and traditional term weighting methods for automatic text categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 721–735 (2009)CrossRefGoogle Scholar
  7. 7.
    Wu, D., Tang, X.J.: Preliminary analysis of Baidu hot words. In: Proceedings of the 11th Workshop of Systems Science and Management Science of Youth and 7th Conference of Logistic Systems Technology, pp. 478–483. Wuhan University of Science and Engineering Press (2011) (in Chinese)Google Scholar
  8. 8.
    Tang, X.: Qualitative meta-synthesis techniques for analysis of public opinions for in-depth study. In: Zhou, J. (ed.) Complex 2009. LNICST, vol. 5, pp. 2338–2353. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Zheng, R., Shi, K., Li, S.: The Influence Factors and Mechanism of Societal Risk Perception. In: Zhou, J. (ed.) Complex 2009. LNICST, vol. 5, pp. 2266–2275. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Chang, C.C., Lin, C.J.: libsvm2.8.3.,
  11. 11.
    Zhang, W., Yoshida, T., Tang, X.: A Study on Multi-word Extraction from Chinese Documents. In: Ishikawa, Y., He, J., Xu, G., Shi, Y., Huang, G., Pang, C., Zhang, Q., Wang, G. (eds.) APWeb 2008 Workshops. LNCS, vol. 4977, pp. 42–53. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yang Hu
    • 1
  • Xijin Tang
    • 1
  1. 1.Institute of Systems Science, Academy of Mathematics and Systems ScienceChinese Academy of SciencesP.R. China

Personalised recommendations