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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\u2010recommendation web\u2010based business sites with the help of a collection strategy and a swarm\u2010based improvement system. At first, the client surveys of the items from web\u2010based business locales with a few features were gathered and, afterward, a fuzzy <jats:italic>c<\/jats:italic>\u2010means (FCM) grouping strategy to group the features for a less demanding procedure was utilized. Also, the novelty of this work\u2014the Dragonfly Algorithm (DA)\u2014recognizes ideal features of the items in sites, and an advanced ideal feature\u2010based positioning procedure will be directed to discover, at long last, which web\u2010based 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.<\/jats:p>","DOI":"10.1155\/2018\/3569351","type":"journal-article","created":{"date-parts":[[2018,9,6]],"date-time":"2018-09-06T23:38:46Z","timestamp":1536277126000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clustering"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7844-462X","authenticated-orcid":false,"given":"S. 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