Advertisement

Text Classification Algorithm Based on SLAS-C

  • Zhichao Yin
  • Jun Xiang
  • Chunyong Yin
  • Jin Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)

Abstract

Nowadays, mobile marketing is becoming increasingly important both strategically and economically because of the mobile devices. Short text is becoming a popular text form which can be seen in many fields such as network news, QQ messages, comments in BBS and so forth. Besides, our mobile devices also contain a lot of data of short text. To extract useful information from the short text more efficiently, this paper proposes SLAS (semi-supervised learning method and SVM classifier) and CART (classification and regression tree) to improve the traditional methods, which can classify massive short texts to mining the useful information from the short texts. The experiment also shows a better result than before, which has a more than 10% increase, including precision rate, recall rate and F1 value, besides, the running time is reduced by half than the KNN algorithm.

Keywords

Mobile marketing Semi-supervised learning SVM Big data Short text classification 

Notes

Acknowledgments

This work was funded by the National Natural Science Foundation of China (61772282, 61373134, and 61402234). It was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0901) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET). We declare that we do not have any conflicts of interest to this work.

References

  1. 1.
    Deng, N., Tian, Y.: New method in data mining—support vector machine (SVM), vol. 16, no. 2, pp. 113–126. Science Press (2004)Google Scholar
  2. 2.
    Breiman, L., Friedman, J.H., Olshen, R.A., et al.: Classification and regression tree. Wadsworth Int. Group 37(15), 237–251 (1984)Google Scholar
  3. 3.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13(1), 21–27 (1967)CrossRefGoogle Scholar
  4. 4.
    Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42–49 (1999)Google Scholar
  5. 5.
    Fan, Y., Liu, H.: Research on Chinese short text classification based on Wikipedia. Mod. Libr. Inf. Technol. 24(3), 47–52 (2012)MathSciNetGoogle Scholar
  6. 6.
    Banerjee, S., Ramanathan, K., Gupta, A.: Clustering short texts using Wikipedia. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 787–788 (2007)Google Scholar
  7. 7.
    Lin, X., Zhang, M., Bao, X.: Short text classification method based on concept network. Comput. Eng. 36(21), 4–10 (2010)Google Scholar
  8. 8.
    Li, X., Pang, J., et al.: Deep neural network for short-text sentiment classification. In: International Conference on Database Systems for Advanced Applications, pp. 168–175 (2016)Google Scholar
  9. 9.
    Francisco, P., Julián-Iranzo, P., et al.: Classifying unlabeled short texts using a fuzzy declarative approach. Lang. Resour. Eval. 47(1), 151–178 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Zhichao Yin
    • 1
  • Jun Xiang
    • 2
  • Chunyong Yin
    • 2
  • Jin Wang
    • 3
  1. 1.No. 1 Middle SchoolNanjingChina
  2. 2.School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologyNanjing University of Information Science and TechnologyNanjingChina
  3. 3.College of Information EngineeringYangzhou UniversityYangzhouChina

Personalised recommendations