E-Commerce Production-Profit Enhancement by Customer Behavior Analysis on Social Network Data

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R Sudha, Dr N Uma Maheswari, Dr. R Venkatesh


One of the drastically growing applications under bigdata analytics is consumer behavior analysis for enhancing the e-commerce industry. This industry requires a useful analytics tool for understanding the online customer those who are interacting in the e-commerce site. Most of the marketing people are using the internet to market their company products. Online customers share their opinionson various social websites, such as Facebook, Twitter, and Instagram. By analyzing the tweets and comments, customer behavior can be identified. In this paper, distance-based clustering and Multi-Class Support Vector Machine based classification are applied to the data for predicting customer behavior. From the experiment, it is identified that the proposed method is highly suitable for a small size dataset.

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