Negin Ziaei, Hamed Fazlollahtabar

With the advancement of online shopping technology, it has become the first choice for most consumers. The activity of online stores in this competitive business space should be in line with the expectations of their customers. Understanding, collecting, maintaining and organize data in online stores makes it easier for managers to decide. So, in this research, we examine the textual and non-textual of user opinions and reviews. We use rapid miner software and text mining. In this research, the processes are aimed at finding active users, analyze the user type and their suggestions, analyzing the strengths and weaknesses of the products, and categorizing them with the K-NN and Naïve Bayes algorithms. Finally, suggestions were made to increase loyalty and improve business using the results obtained from the processes.


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