Mining aspects of customer’s review on the social network

Abstract

Abstract This study represents an efficient method for extracting product aspects from customer reviews and give solutions for inferring aspect ratings and aspect weights. Aspect ratings often reflect the user’s satisfaction on aspects of a product and aspect weights reflect the degree of importance of the aspects posed by the user. These tasks therefore play a very important role for manufacturers to better understand their customers’ opinion on their products and services. The study addresses the problem of aspect extraction by using aspect words based on conditional probability combined with the bootstrap technique. To infer the user’s rating for aspects, a supervised approach called the Naïve Bayes classification method is proposed to learn the aspect ratings in which sentiment words are considered as features. The weight of an aspect is estimated by leveraging the frequencies of aspect words within each review and the aspect consistency across all reviews. Experimental results show that the proposed method obtains very good performance on real world datasets in comparison with other state-of-the-art methods.

Keywords

• Aspect extraction
• Aspect rating
• Aspect weight
• Conditional probability
• Core term
• Naive Bayes