An Efficient Research of Auto Ranking of Amazon Research using Regression Models
Anshul Rawat1, Neetesh Gupta2
1Anshul Rawat, M.Tech, Research Scholar, Department of CSE, TIT-S Bhopal (Madhya Pradesh), India.
2Neetesh Gupta, Professor Head, Department of CSE, TIT-S Bhopal (Madhya Pradesh), India.
Manuscript received on 15 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 06 September 2019 | PP: 27-31 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F10060886S19/19©BEIESP | DOI: 10.35940/ijeat.F1006.0886S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Before providing services to customers it is very important to know about the requirements as well as the services or products we are providing them contains opinions of other customers in which manner like-it is a positive review from the customer or the negative review. Since, an opinion plays a very important role in purchasing anything. There are some sites running online for the purpose of providing goods to the customers also they focused onto taking the decision over the posting reviews whether it is a positive response or negative. The motive of the work is to analyse the social data or products reviews simultaneously, and then create a model that will automatically create a model for product review. This paper bringing the continuous audits from a web based business website amazon and apply different content mining methods to pre-process the information and afterward apply an AI approach through which results will assess the viability of surveys through an outstanding measure for decency of fit. In this paper a development model with a computational cost model is utilized. The improved cost model with the word handling and positioning is utilized in given research.
Keywords: Artificial Intelligence, Helpfulness Index, Goodness of Fit, Word Count.
Scope of the Article: Probabilistic Models and Methods