Calculating Effective Product Marketing on E-Commerce Applications based on Customer Rating using big data
R Vijayan1, V Mareeswari2, S Prasanna3, C Navaneethan4, K Yaswanth5
1R Vijayan, Associate Professor, Department of Computer Science and Engineering, School of Information Technology and Engineering, VIT-Vellore Institute of Technology, Vellore, (Tamil Nadu), India.
2V Mareeswari, Assistant Professor (Senior), Department of Computer Science and Engineering, School of Information Technology and Engineering (SITE), Vellore Institute of Technology (VIT), Vellore, (Tamil Nadu), India.
3C Navaneethan, Associate Professor, Department of Computer Science and Engineering, VIT, Vellore, (Tamil Nadu), India.
4S Prasanna, Associate Professor at School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, (Tamil Nadu), India.
5K Yaswanth, School of Information Technology and Engineering (SITE), Vellore Institute of Technology (VIT), Vellore, (Tamil Nadu), India
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 5130-5136 | Volume-8 Issue-12, October 2019. | Retrieval Number: L27611081219/2019©BEIESP | DOI: 10.35940/ijitee.L2761.1081219
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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: While purchasing online products, our priority is to consider online rating regarding the product. Based on the customer rating of products it can be possible to determine their lifetime, sales and that impacts the ability to be maintained at a certain rate or level of a product in the market. The rating is considered as datasets where they are being extracted from E-Commerce websites. In specific, consider the review content, product ratings and divide product lifetime. While collecting the relevant information from our review data we consider the data into two categories as positive data and negative data. When a user posted a review, we consider the keywords to state the review was good or bad and their rating behaviors, these extracted scores can be correlated with their rating with product popularity. The product popularity can be considered by the total number of purchases of the product and the rating given to the product. It also can be analyzed by product ratings that indicate that raters’ ratings are likely to influence product popularity. Taking different e-commerce datasets to extract review content and obtaining relevant information from the review data can analyze and predict the product’s early raters and product marketing.
Keywords: E-commerce, Products. Rating. Content. Data Analysis
Scope of the Article: E-commerce