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This work studies how we can obtain feature-level ratings of the mobileproducts from the customer reviews and review votes to influence decisionmaking, both for new customers and manufacturers. Such a rating system gives amore comprehensive picture of the product than what a product-level ratingsystem offers. While product-level ratings are too generic, feature-levelratings are particular; we exactly know what is good or bad about the product.There has always been a need to know which features fall short or are doingwell according to the customer's perception. It keeps both the manufacturer andthe customer well-informed in the decisions to make in improving the productand buying, respectively. Different customers are interested in differentfeatures. Thus, feature-level ratings can make buying decisions personalized.We analyze the customer reviews collected on an online shopping site (Amazon)about various mobile products and the review votes. Explicitly, we carry out afeature-focused sentiment analysis for this purpose. Eventually, our analysisyields ratings to 108 features for 4k+ mobiles sold online. It helps indecision making on how to improve the product (from the manufacturer'sperspective) and in making the personalized buying decisions (from the buyer'sperspective) a possibility. Our analysis has applications in recommendersystems, consumer research, etc.

Feature-level Rating System using Customer Reviews and Review Votes