Analytics in Support of E-Commerce Systems Using Machine Learning

Analysis of customer relationships based on their satisfaction is reaching a practical and motivating success factor for the growth of every company. Web intelligence describes the scientific development that uses information technology and artificial intelligence for new frameworks, services, and products provided by the web. This chapter aims to present the model of analyzing the users’ sentiments from their online reviews on an e-commerce platform using machine-learning classifiers namely Naive Bayes, Logistic regression, Support Vector Machine, and Neural Network. For data analysis, Latent semantic analysis has been applied to examine the most frequent words used in online reviews. Finally, customers’ interest in online shopping analysis has been performed to classify the customers’ sentiment from their posted reviews on the e-commerce platform. In addition, we compared the performance results of these classifiers on the e-commerce dataset. The results reveal that the Logistic regression classifier has performed better than all the other three classifiers.