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Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clustering

Author

Listed:
  • S. K. Lakshmanaprabu
  • K. Shankar
  • Deepak Gupta
  • Ashish Khanna
  • Joel J. P. C. Rodrigues
  • Plácido R. Pinheiro
  • Victor Hugo C. de Albuquerque

Abstract

Sites for web-based shopping are winding up increasingly famous these days. Organizations are anxious to think about their client purchasing conduct to build their item deal. Internet shopping is a method for powerful exchange among cash and merchandise which is finished by end clients without investing a huge energy spam. The goal of this paper is to dissect the high-recommendation web-based business sites with the help of a collection strategy and a swarm-based improvement system. At first, the client surveys of the items from web-based business locales with a few features were gathered and, afterward, a fuzzy c -means (FCM) grouping strategy to group the features for a less demanding procedure was utilized. Also, the novelty of this work—the Dragonfly Algorithm (DA)—recognizes ideal features of the items in sites, and an advanced ideal feature-based positioning procedure will be directed to discover, at long last, which web-based business webpage is best and easy to understand. From the execution, the outcomes demonstrate the greatest exactness rate, that is, 94.56% compared with existing methods.

Suggested Citation

  • S. K. Lakshmanaprabu & K. Shankar & Deepak Gupta & Ashish Khanna & Joel J. P. C. Rodrigues & Plácido R. Pinheiro & Victor Hugo C. de Albuquerque, 2018. "Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clustering," Complexity, Hindawi, vol. 2018, pages 1-9, September.
    • Handle: RePEc:hin:complx:3569351
    DOI: 10.1155/2018/3569351
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