Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A filtering of message in online social network using hybrid classifier

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In emerging trend of society based on online social networking (OSN), it is useful for associative with social relationship. It is mainly used to share all moments via messages with their private and public sector. In this social network, most of the people used to send indecent text to others. So, non-essential text will be neglected from user to view desirable messages on their OSN walls. This paper explores the use of short text classifier includes latent semantic analysis for extracting meaningful text to present on their user wall by preventing useless messages. The proposed algorithm concerns hybrid combination of text classifiers such as support vector machine SVM–ELM, SVM–NAÏVE, NAÏVE–ELM, and NAÏVE–artificial neuro-fuzzy inference systems. Comparative study of these techniques contributes which will gives higher prediction accuracy in evaluation process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Adomavicius, A., Tuzhilin, G.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  2. Carminati, B., Ferrari, E., Binaghi, E., Vanetti, M., Carullo, M.: A system to filter unwanted messages from OSN user walls. IEEE Trans. Knowl. Data Eng. 25(2), 285–297 (2013)

    Article  Google Scholar 

  3. Kumar, H., Jain, S., Srivastava, R.: Risk analysis of online social networks. In: International Conference on Computing, Communication and Automation (ICCCA), pp. 846–851. IEEE (2016)

  4. Li, J., Yan, H., Liu, Z.: Location-sharing systems with enhanced privacy in mobile online social networks. IEEE Syst. J. 11(2), 439–448 (2017)

    Article  Google Scholar 

  5. Liu, X.: An improved clustering-based collaborative filtering recommendation algorithm. Clust. Comput. 20(2), 1281–1288 (2017)

    Article  Google Scholar 

  6. Chau, M., Chen, H.: A machine learning approach to web page filtering using content and structure analysis. Decis. Support Syst. 44(2), 482–494 (2008)

    Article  Google Scholar 

  7. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: European Conference on Machine Learning, pp. 137–142. ACM (1998)

  8. Zhou, S., Guan, J.: Evaluation and construction of training corpuses for text classification: a preliminary study. In: International Conference on Application of Natural Language to Information System, pp. 97–108. Springer, Berlin (2002)

  9. Joachims, T.: Text categorization with Support Vector Machines: learning with many relevant features. In: 10th European Conference on Machine Learning Chemnitz, pp. 137–142. Springer, Berlin (1998)

  10. Novovicova, J., Malik, A., Pudil, P.: Feature selection using improved mutual information for text classification. In: International Workshops on Statistical, Techniques, Structural and Syntactic Pattern Recognition, pp. 1010–1017. Springer, Berlin (2004)

  11. Vanetti, M., Binaghi, E., Carminati, B., Carullo, M., Ferrari, E.: Content-based filtering in online social networks. In: International Workshop on Privacy and Security Issues in Data Mining and Machine Learning (PSDML ’10). Springer, Berlin (2011)

  12. Cho, J.-H., Alsmadi, I., Xu, D.: Privacy and social capital in online social networks. In: IEEE Global Communications Conference (GLOBECOM), pp. 1–7. IEEE (2016)

  13. Bao, Y., Ishii, N.: Combining multiple KNN classifiers for text categorization by reducts. In: International Conference on Discovery Science, pp. 340–347. Springer, Berlin (2002)

  14. Nardiello, P., Sebastiani, F., Sperduti, A.: Discretizing continuous attributes in ada boost for text categorization. In: European Conference on Information Retrieval, pp. 320–334. Springer, Berlin (2003)

  15. Zeng, Y., Xing, L.: A study of online social network privacy via the TAPE framework. J. Sel. Top. Signal Process. 9(7), 1270–1284 (2015)

    Article  Google Scholar 

  16. Sebastiani, F.: Machine learning in automated text categorization. Comput. Surv. 34(1), 1–47 (2002)

    Article  MathSciNet  Google Scholar 

  17. Paramasivam, S., Arumugam, R.: Accurate rotor position estimation for switched reluctance motor using ANFIS. In: Conference on Convergent Technologies for Asia–Pacific Region, vol. 4, pp. 1493–1497. IEEE (2003)

  18. Foltz, P.W., Dumais, S.T.: Personalized information delivery: analysis of information filtering methods. Spec. Issue Inf. Filter. 35(12), 51–60 (1992)

    Google Scholar 

  19. Hull, D.: Improving text retrieval for the routing problem using latent semantic indexing. In: 17th Annual International Conference on Research and Development in Information Retrieval, pp. 282–291. ACM (1994)

  20. Han, X., Zu, G., Ohyama, W., Wakabayashi, T., Kimura, F.: Accuracy improvement of automatic text classification based on feature transformation and multi-classifier combination. In: Content Computing, pp. 463–468. Springer, Berlin (2004)

  21. Sebastiani, F.: Machine learning in automated text categorization. Comput. Surv. 34(1), 1–47 (2002)

    Article  MathSciNet  Google Scholar 

  22. Zu, G., Ohyama, W., Wakabayashi, T., Kimura, F.: Accuracy improvement of automatic text classification based on feature transformation. In: Proceedings of the ACM Symposium on Document Engineering, pp. 118–120. ACM (2003)

Download references

Author information

Authors and Affiliations

  1. Research and Development Centre, Bharathiar University, Coimbatore, 641046, India

    M. Srividya

  2. Nehru Institute of Engineering and Technology, T. M. Palayam, Coimbatore, 641105, India

    M. S. Irfan Ahmed

Authors
  1. M. Srividya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. S. Irfan Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Srividya.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Srividya, M., Ahmed, M.S.I. A filtering of message in online social network using hybrid classifier. Cluster Comput 22 (Suppl 5), 11079–11086 (2019). https://doi.org/10.1007/s10586-017-1300-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1300-y

Keywords

Search

Navigation