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Multi-Attribute Online Decision-Making Driven by Opinion Mining

Author

Listed:
  • Azra Shamim

    (Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah 23218, Saudi Arabia
    Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Muhammad Ahsan Qureshi

    (Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah 23218, Saudi Arabia)

  • Farhana Jabeen

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan)

  • Misbah Liaqat

    (Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah 23218, Saudi Arabia)

  • Muhammad Bilal

    (School of Computer Science and Engineering, Taylor’s University, Subang Jaya 47500, Malaysia
    Centre for Data Science and Analytics (C4DSA), Taylor’s University, Subang Jaya 47500, Malaysia)

  • Yalew Zelalem Jembre

    (Department of Electronics, Keimyung University, Daegu 42601, Korea)

  • Muhammad Attique

    (Department of Software, Sejong University, Seoul 05006, Korea)

Abstract

With the evolution of data mining systems, the acquisition of timely insights from unstructured text is an organizational demand which is gradually increasing. The existing opinion mining systems have a variety of properties, such as the ranking of products’ features and feature level visualizations; however, organizations require decision-making based upon customer feedback. Therefore, an opinion mining system is proposed in this work that ranks reviews and features based on novel ranking schemes with innovative opinion-strength-based feature-level visualization, which are tightly coupled to empower users to spot imperative product features and their ranking from enormous reviews. Enhancements are made at different phases of the opinion mining pipeline, such as innovative ways to evaluate review quality, rank product features and visualize opinion-strength-based feature-level summary. The target user groups of the proposed system are business analysts and customers who want to explore customer comments to gauge business strategies and purchase decisions. Finally, the proposed system is evaluated on a real dataset, and a usability study is conducted for the proposed visualization. The results demonstrate that the incorporation of review and feature ranking can improve the decision-making process.

Suggested Citation

  • Azra Shamim & Muhammad Ahsan Qureshi & Farhana Jabeen & Misbah Liaqat & Muhammad Bilal & Yalew Zelalem Jembre & Muhammad Attique, 2021. "Multi-Attribute Online Decision-Making Driven by Opinion Mining," Mathematics, MDPI, vol. 9(8), pages 1-25, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:833-:d:534089
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    References listed on IDEAS

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    1. Erick Kauffmann & Jesús Peral & David Gil & Antonio Ferrández & Ricardo Sellers & Higinio Mora, 2019. "Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
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    Cited by:

    1. Ma, Guanhua & Ma, Junhua & Li, Hao & Wang, Yiming & Wang, Zhaohua & Zhang, Bin, 2022. "Customer behavior in purchasing energy-saving products: Big data analytics from online reviews of e-commerce," Energy Policy, Elsevier, vol. 165(C).

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