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Assessing web sites quality: A systematic literature review by text and association rules mining

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  • Rekik, Rim
  • Kallel, Ilhem
  • Casillas, Jorge
  • Alimi, Adel M.

Abstract

Nowadays society is deeply affected by web content. A web site, regardless of its category, can provide or not for users their needs. To identify its strengths and weaknesses, a process of analyzing and assessing its quality, via some criteria, is necessary. Assessing web sites is considered as a Multiple Criteria Decision Making problem (MCDM), with a massive number of criteria; a reduction phase is needed. This paper presents, firstly a Systematic Literature Review (SLR) to identify the purposes of recent researches from the assessment and determine the affected categories; secondly, it proposes a process of collecting and extracting data (criteria featuring web sites) from a list of studies. Text mining is applied for this SLR to construct a dataset. Then, a method based on Apriori algorithm is assigned and implemented to find association rules between criteria and the category of the web site, and to get a set of frequent criteria. This paper also presents a review on soft computing assessing methods. It aims to help the research community to have a scope in existing research and to derive future developments. The obtained results motivate us to further probe datasets and association rule mining.

Suggested Citation

  • Rekik, Rim & Kallel, Ilhem & Casillas, Jorge & Alimi, Adel M., 2018. "Assessing web sites quality: A systematic literature review by text and association rules mining," International Journal of Information Management, Elsevier, vol. 38(1), pages 201-216.
    • Handle: RePEc:eee:ininma:v:38:y:2018:i:1:p:201-216
    DOI: 10.1016/j.ijinfomgt.2017.06.007
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    References listed on IDEAS

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    1. Fu Hsiang Chen & Gwo-Hshiung Tzeng & Chih Chieh Chang, 2015. "Evaluating the Enhancement of Corporate Social Responsibility Websites Quality Based on a New Hybrid MADM Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(03), pages 697-724.
    2. Heradio, Rubén & Cabrerizo, Francisco Javier & Fernández-Amorós, David & Herrera, Manuel & Herrera-Viedma, Enrique, 2013. "A fuzzy linguistic model to evaluate the quality of Library 2.0 functionalities," International Journal of Information Management, Elsevier, vol. 33(4), pages 642-654.
    3. Ozmen-Ertekin, Dilruba & Ozbay, Kaan, 2012. "Dynamic data maintenance for quality data, quality research," International Journal of Information Management, Elsevier, vol. 32(3), pages 282-293.
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    Cited by:

    1. Jiang, Cuiqing & Yin, Chang & Tang, Qian & Wang, Zhao, 2023. "The value of official website information in the credit risk evaluation of SMEs," Journal of Business Research, Elsevier, vol. 169(C).
    2. Sizo, Amanda & Lino, Adriano & Reis, Luis Paulo & Rocha, Álvaro, 2019. "An overview of assessing the quality of peer review reports of scientific articles," International Journal of Information Management, Elsevier, vol. 46(C), pages 286-293.
    3. Amiri, Babak & Karimianghadim, Ramin, 2024. "A novel text clustering model based on topic modelling and social network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. EL Idrissi, Touria & Idri, Ali & Bakkoury, Zohra, 2019. "Systematic map and review of predictive techniques in diabetes self-management," International Journal of Information Management, Elsevier, vol. 46(C), pages 263-277.

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