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Mining consumer reviews to generate ratings of different product attributes while producing feature-based review-summary

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  • Akshay Kangale
  • S. Krishna Kumar
  • Mohd Arshad Naeem
  • Mark Williams
  • M. K. Tiwari

Abstract

With the massive growth of the internet, product reviews increasingly serve as an important source of information for customers to make choices online. Customers depend on these reviews to understand users’ experience, and manufacturers rely on this user-generated content to capture user sentiments about their product. Therefore, it is in the best interest of both customers and manufacturers to have a portal where they can read a complete comprehensive summary of these reviews in minimum time. With this in mind, we arrived at our first objective which is to generate a feature-based review-summary. Our second objective is to develop a predictive model to know the next week's product sales based on numerical review ratings and textual features embedded in the reviews. When it comes to product features, every user has different priorities for different features. To capture this aspect of decision-making, we have designed a new mechanism to generate a numerical rating for every feature of the product individually. The data have been collected from a well-known commercial website for two different products. The validation of the model is carried out using a crowd-sourcing technique.

Suggested Citation

  • Akshay Kangale & S. Krishna Kumar & Mohd Arshad Naeem & Mark Williams & M. K. Tiwari, 2016. "Mining consumer reviews to generate ratings of different product attributes while producing feature-based review-summary," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(13), pages 3272-3286, October.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:13:p:3272-3286
    DOI: 10.1080/00207721.2015.1116640
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    References listed on IDEAS

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    Cited by:

    1. Yao Jiao & Yu Yang & Hongshan Zhang, 2019. "An integration model for generating and selecting product configuration plans," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1291-1302, March.
    2. Yao Jiao & Yu Yang, 2019. "A product configuration approach based on online data," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2473-2487, August.
    3. Soumya Mukhopadhyay, 2018. "Opinion mining in management research: the state of the art and the way forward," OPSEARCH, Springer;Operational Research Society of India, vol. 55(2), pages 221-250, June.
    4. Bag, Sujoy & Tiwari, Manoj Kumar & Chan, Felix T.S., 2019. "Predicting the consumer's purchase intention of durable goods: An attribute-level analysis," Journal of Business Research, Elsevier, vol. 94(C), pages 408-419.

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