Feature-based Customer Review Mining

The large amount of the information is a big challenge to the customer’s patience to read all the feedbacks. Topical classification and sentimental classification are proposed to be used in information classification. Several machine learning methods, such as the Naive Bayes, Maximum Entropy classification, and Support Vector Machines are good approaches to solve this problem. However, classic sentimental classification does not find what the reviewer liked or disliked. Our approach generalizes an overall rating and user comments on several features for each product. It calculates an overall rating of the product based on PIM-IR algorithm and generalizes these comments on features using feature-based classification.