  This work studies how we can obtain feature-level ratings of the mobile
products from the customer reviews and review votes to influence decision
making, both for new customers and manufacturers. Such a rating system gives a
more comprehensive picture of the product than what a product-level rating
system offers. While product-level ratings are too generic, feature-level
ratings 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 doing
well according to the customer's perception. It keeps both the manufacturer and
the customer well-informed in the decisions to make in improving the product
and buying, respectively. Different customers are interested in different
features. 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 a
feature-focused sentiment analysis for this purpose. Eventually, our analysis
yields ratings to 108 features for 4k+ mobiles sold online. It helps in
decision making on how to improve the product (from the manufacturer's
perspective) and in making the personalized buying decisions (from the buyer's
perspective) a possibility. Our analysis has applications in recommender
systems, consumer research, etc.
