Skip to main content
SpringerLink
Log in

Comments’ Extraction by User ID, Keywords, and Anchor Texts from the Thread of a Bulletin Board System

  • Published:
The Review of Socionetwork Strategies Aims and scope Submit manuscript

Abstract

Bulletin Board Systems (BBSs) on the Web are used by many users posting comments on threads. Each thread carries a subject of discussion. Most users post comments related to a subject. However, unrelated comments are also posted. The unrelated comments distract users and diminish their understanding of the entire story of a thread. Comments unrelated to a subject have to be filtered automatically. This paper proposes a method that extracts comments related to a thread subject from a thread of BBS. The method extracts two types of comments: comments related to a thread subject (main comments), and comments related to main comments (sub-comments). The main comments are extracted by a user ID and keywords in the comments. The sub-comments are extracted by explicit and implicit anchor texts in the comments. We experimented with the proposed method and verified that comments extracted by the proposed method support participants in understanding the entire story of a thread.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Boguraev, B. K., Neff, M. S.: Discourse Segmentation in aid of Document Summarization. In Proceedings of Hawaii International Conference on System Sciences. 1-10, 2000

  2. Hearst M. A.: TextTiling: Segmenting text into multi-paragraph subtopic passages. Computational Linguistics. 23(1), 33–64 (1997)

    Google Scholar 

  3. Beeferman, D., Berger, A., Lafferty, J.: Statistical Models for Text Segmentation. Machine Learning. 34(1-3), 177-210, 1999

  4. Grosz B. J., Sidner C. L.: Attention, Intention, and the Structure of Discourse. Computational Linguistics. 12(3), 175–204 (1986)

    Google Scholar 

  5. Zechner K.: Automatic Summarization of Open-domain Multiparty Dialogues in Diverse Genres. Computational Linguistics. 28, 447–485 (2002)

    Article  Google Scholar 

  6. Mani, I.: Automatic Summarization. John Benjamins Publishing Company, 2001

  7. Matsuo, Y., Ohsawa, Y., Ishizuka, M.: Mining Messages in an Electronic Message Board by Repetition of Words. The Second International Workshop on Chance Discovery, Pacific Rim International Conference on AI. 2002

  8. Sagara, N., Sunayama, W., Yachida, M.: Informative Summarization Method by Key Sentences Extraction Considering Sub-Topics. The IEICE Transactions (Japanese Edition). J90-D(2), 427-440, 2007

Download references

Author information

Authors and Affiliations

  1. College of Information Sciences and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, 525-8577, Shiga, Japan

    Yoko Nishihara

  2. Graduate School of Information Sciences, Hiroshima City University, 3-4-1 Ozuka-higashi, Asa-minami, 731-3194, Hiroshima, Japan

    Wataru Sunayama

  3. Faculty of Engineering, Hiroshima Institute of Technology, 2-1-1 Miyake, Saeki, 731-5193, Hiroshima, Japan

    Kazunori Nishimura

Authors
  1. Yoko Nishihara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wataru Sunayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kazunori Nishimura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yoko Nishihara.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nishihara, Y., Sunayama, W. & Nishimura, K. Comments’ Extraction by User ID, Keywords, and Anchor Texts from the Thread of a Bulletin Board System. Rev Socionetwork Strat 8, 35–49 (2014). https://doi.org/10.1007/s12626-014-0042-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12626-014-0042-z

Keywords

Search

Navigation