Skip to main content
SpringerLink
Log in
SpringerLink
Log in

New Method for Extracting Keyword for the Social Actor

  • Conference paper
Intelligent Information and Database Systems (ACIIDS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8397))

Included in the following conference series:

Abstract

In this paper we study the relationship between query and search engine by exploring some properties and also applying their relations to extract keyword for any social actor by proposing new method. The proposed approach based on considering the result of search engine in the singleton and doubleton. In this paper, we develop a novel method for extracting keyword automatically from Web with mirror shade concept (M2M). Results show the potential of the proposed approach, in experiment we get that the performance (recall and precision) of keyword depend on both weights (singleton and tfidf) and the distance of them.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abu-Nimeh, S., Nappa, D., Wang, X., Nair, S.: A comparison of machine learning techniques for phising detection. In: Proceedings of the Anti-Phising Working Groups 2nd Annual eCrime Researhers Summit, pp. 60–69 (2007)

    Google Scholar 

  2. Adriani, M.: Using statistical term similarity for sense disambiguation in cross-language information retrieval. Information Retrieval 2, 69–80 (2000)

    Article  Google Scholar 

  3. Bergholz, A., Chang, J.-H., Paass, G., Reichartz, F., Strobel, S.: Improved phishing detection using model-based features. In: Proceedings of Fifth Conference on Email and Anti-Spam (2008)

    Google Scholar 

  4. Bergholz, A., Beer, J.D., Glahn, S., Moens, M.-F., Paass, G., Strobel, S.: New filtering approaches for phishing email. Journal of Computer Security (2009)

    Google Scholar 

  5. Buckley, C., Dimmick, D., Soboroff, I., Voorhees, E.: Bias and the limits of pooling for large collections. Information Retrieval 10, 491–508 (2007)

    Article  Google Scholar 

  6. Ercan, G., Cicekli, I.: Using lexical chains for keyword extraction. Information Processing and Management 43, 1705–1714 (2007)

    Article  Google Scholar 

  7. Fette, I., Sadeh, N., Tomasic, A.: Learning to detect phising emails. In: ACM Proceedings of the 16th International Conference on World Wide Web, pp. 649–656 (2007)

    Google Scholar 

  8. HaCohen-Kerner, Y.: Automatic extraction of keywords froom abstracts. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2773, pp. 843–849. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  9. Kim, B.-M., Li, Q., Lee, K., Kang, B.-Y.: Extraction of representative keywords considering co-occurrence in positive documents. In: Wang, L., Jin, Y. (eds.) FSKD 2005. LNCS (LNAI), vol. 3614, pp. 752–761. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  10. L’Huillier, G., Hevia, A., Weber, R., Ríos, S.: Laten semantic analysis and keyword extraction for phising classification. In: IEEE International Conference on Intelligence and Security Informatics (ISI), Vancouver, BC, Canada, pp. 129–131 (2010)

    Google Scholar 

  11. Liang, B., Tang, J., Li, J., Wang, K.-H.: Keyword extraction based peer clustering. In: Jin, H., Pan, Y., Xiao, N., Sun, J. (eds.) GCC 2004. LNCS, vol. 3251, pp. 827–830. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Nasution, M.K.M., Noah, S.A.: Superficial method for extracting social network for academic using web snippets. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS (LNAI), vol. 6401, pp. 483–490. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  13. Nasution, M.K.M., Noah, S.A.M.: Extraction of academic social network from online database. In: Noah, S.A.M., et al. (eds.) IEEE Proceeding of 2011 International Conference on Semantic Technology and Information Retrieval, Putrajaya, Malaysia, pp. 64–69. IEEE (2011)

    Google Scholar 

  14. Nasution, M.K.M., Noah, S.A.M., Saad, S.: Social network extraction: Superficial method and information retrieval. In: Proceeding of International Conference on Informatics for Development (ICID 2011), pp. c2-110-c2-115 (2011)

    Google Scholar 

  15. Nasution, M.K.M., Noah, S.A.M.: Information Retrieval Model: A Social Network Extraction Perspective. In: IEEE Proc. of CAMP 2012 (2012)

    Google Scholar 

  16. Nasution, M.K.M.: Simple search engine model: Adaptive properties. Cornell University Library (arXiv:1212.3906v1) (2012)

    Google Scholar 

  17. Nasution, M.K.M.: Simple search engine model: Adaptive properties for doubleton. Cornell University Library (arXiv:1212.4702v1) (2012)

    Google Scholar 

  18. Nasution, M.K.M.: Simple search engine model: Selective properties. Cornell University Library (arXiv:1303.3964v1) (2012)

    Google Scholar 

  19. Nasution, M.K.M. (Mahyuddin): Kaedah dangkal bagi pengekstrakan rangkaian sosial akademik dari Web, Ph.D. Dissertation, Universiti Kebangsaan Malaysia (2013) (in Malay)

    Google Scholar 

  20. Okada, M., Ando, K., Le, S.S., Hayashi, Y., Aoe, J.-I.: An efficient substring search method by using delayed keyword extraction. Information Processing and Management 37, 741–761 (2001)

    Article  MATH  Google Scholar 

  21. Wang, J., Liu, J., Wang, C.: Keyword extraction based on PageRank. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 857–864. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Technology Department, Fakultas Ilmu Komputer dan Teknologi Informasi (Fasilkom-TI) and Centre of Information System, Universitas Sumatera Utara, Medan, 20155 USU, Sumatera Utara, Indonesia

    Mahyuddin K. M. Nasution

Authors
  1. Mahyuddin K. M. Nasution
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Ngoc Thanh Nguyen  & Bogdan Trawiński  & 

  2. Faculty of Engineering, Department of Computer Engineering, King Mongkut’s Institute of Technology Ladkrabang, 10520, Bangkok, Thailand

    Boonwat Attachoo

  3. Software Engineering Program, King Mongkut’s Institute of Technology Ladkrabang, 10520, Bangkok, Thailand

    Kulwadee Somboonviwat

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Nasution, M.K.M. (2014). New Method for Extracting Keyword for the Social Actor. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8397. Springer, Cham. https://doi.org/10.1007/978-3-319-05476-6_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05476-6_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05475-9

  • Online ISBN: 978-3-319-05476-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation