Advertisement

Is SVM+FS Better to Satisfy Decision by Majority?

  • Yao Lin
  • Kohei Yamaguchi
  • Tsunenori Mine
  • Sachio Hirokawa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)

Abstract

Government 2.0 activities have become very attractive and popular. Using the platforms to support the activities, anyone can anytime report issues in a city on the Web and share the reports with other people. Since a variety of reports are posted, officials in the city management section have to give priorities to the reports. However, it is not easy task to judge the importance of the reports since importance judgments vary depending on the officials and consequently the agreement rate becomes low. To remedy the low agreement rate problem of human judgment, it is necessary to create an automatic method to find reports with high priorities. Hirokawa et al. employed the Support Vector Machine (SVM) with word feature selection method (SVM+FS) to detect signs of danger from posted reports because signs of danger is one of high priority issues to be dealt with. However they did not compare the SVM+FS method with other conventional machine learning methods and it is not clear whether or not the SVM+FS method has better performance than the other methods. This paper compared the results of the SVM+FS method with conventional machine learning methods: SVM, Random Forest, and Naïve Bayse with conventional word vectors, an LDA-based document vector, and word embedding by Word2Vec. Experimental results illustrate the validity and effectiveness of the SVM+FS method.

Keywords

Government 2.0 Machine learning Support vector machine 

Notes

Acknowledgement

This work was partially supported by JSPS KAKENHI Grant No. JP15H05708, JP16H02926, and JP17H01843.

References

  1. 1.
    Adachi, Y., Onimura, N., Yamashita, T., Hirokawa, S.: Standard measure and SVM measure for feature selection and their performance effect for text classification. In: Proceedings of the 18th iiWAS2016, pp. 262–266. ACM (2016)Google Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1002 (2003)MATHGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefMATHGoogle Scholar
  4. 4.
    Cresci, S., Cimino, A., Dell ’ Orletta, F., Tesconi, M.: Crisis mapping during natural disasters via text analysis of social media messages. In: International Conference on Web Information Systems Engineering, pp. 250–258 (2015)Google Scholar
  5. 5.
    Hirokawa, S., Suzuki, T., Mine, T.: Machine learning is better than human to satisfy decision by majority. In: WI’17. IEEE/ACM (2017)Google Scholar
  6. 6.
    Imran, M., Castillo, C., Diaz, F., Vieweg, S.: Processing social media messages in mass emergency: a survey. ACM Comput. Surv. (CSUR) 47(4), 67 (2015)CrossRefGoogle Scholar
  7. 7.
    Joachims, T.: Text categorization with Support Vector Machines: Learning with many relevant features. In: ECML 1998: Machine Learing: ECML-98, pp. 137–142 (1998)Google Scholar
  8. 8.
    Joachims, T.: Learning to classify text using support vector machines. Dissertation, Kluwer (2002)Google Scholar
  9. 9.
    McCallum, A., Nigam, K., others.: A comparison of event models for naive bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, vol. 752, pp. 41–48 (1998)Google Scholar
  10. 10.
    Sakai, T., Hirokawa, S.: Feature words that classify problem sentence in scientific article. In: Proceedings of the 14th iiWAS2012. pp. 360–367. ACM (2012)Google Scholar
  11. 11.
    Tomás̆, M.: Statistical language models based on neural networks. Ph.D. thesis, Brno University of Technology (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Yao Lin
    • 1
  • Kohei Yamaguchi
    • 1
  • Tsunenori Mine
    • 1
  • Sachio Hirokawa
    • 1
  1. 1.Department of Advanced Information TechnologyKyushu UniversityNishi-ku, FukuokaJapan

Personalised recommendations