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Machine Learning Techniques in Web Content Mining: A Comparative Analysis

Author

Listed:
  • Basavaraj S. Anami

    (KLE Institute of Technology, HUBLI, India)

  • Ramesh S. Wadawadagi

    (Basaveshwar Engineering College, BAGALKOT, India)

  • Veerappa B. Pagi

    (Basaveshwar Engineering College, BAGALKOT, India)

Abstract

With incessantly growing amount of information published over Web pages, the World Wide Web (WWW) has become prolific in the field of data mining research. The heterogeneous and semi-structured nature of Web data has made the process of automated discovery a challenging issue. Web Content Mining (WCM) essentially uses data mining techniques to effectively discover knowledge from Web page contents. The intent of this study is to provide a comparative analysis of Machine Learning (ML) techniques available in the literature for WCM. For analysis, the article focuses on issues such as representation techniques, learning methods, datasets used and performance of each method as a criterion. The survey observes that some of the traditional ML algorithms have been efficiently used to work on Web data. Finally, the paper concludes citing some promising issues for further research in this domain.

Suggested Citation

  • Basavaraj S. Anami & Ramesh S. Wadawadagi & Veerappa B. Pagi, 2014. "Machine Learning Techniques in Web Content Mining: A Comparative Analysis," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 1-12.
    • Handle: RePEc:wsi:jikmxx:v:13:y:2014:i:01:n:s0219649214500051
    DOI: 10.1142/S0219649214500051
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    Cited by:

    1. Altarturi, Hamza H.M. & Saadoon, Muntadher & Anuar, Nor Badrul, 2020. "Cyber parental control: A bibliometric study," Children and Youth Services Review, Elsevier, vol. 116(C).
    2. Madan Lal Yadav & Basav Roychoudhury, 2019. "Effectiveness of Domain-Based Lexicons vis-à-vis General Lexicon for Aspect-Level Sentiment Analysis: A Comparative Analysis," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 1-18, September.
    3. Samuel Zanferdini Oliva & Livia Oliveira-Ciabati & Denise Gazotto Dezembro & Mário Sérgio Adolfi Júnior & Maísa Carvalho Silva & Hugo Cesar Pessotti & Juliana Tarossi Pollettini, 2021. "Text structuring methods based on complex network: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1471-1493, February.

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