Customer’s opinion mining from online reviews using intelligent rules with machine learning techniques

In the field of marketing, many surveys were conducted to analyze the customer satisfaction on products in their online purchases. But the real view of customers about the product is mirrored in the customer’s online reviews (COR) given by them, while they purchase the product online. This paper is the one for analyzing and distinguishing the real view about the customer satisfaction by reviewing their opinions for the product which they buy. As a part of opinion mining, the polarity of the specific word is extracted and classifies the review as positive or negative using Naïve Bayes classifier. And this creates a genuine view about the product from the customer point of view. The real opinion about the customer view on online shopping is going to be distinguished according to the intelligent rules generated based on the hypothesis. Intelligent rules help to classify the reviews by extracting the real opinion of the customer based on the feature they specified for the product which is purchased by the consumer. This kind of feature-based review classification supports the purchase of new users when they approach online shopping. This work also projects the customer view about which feature they really need and also feel good, from their review representation.