A five-star quality rating is one of the most
widely used systems for evaluating items. However, it has two fundamental
limitations: 1) the rating for one item cannot describe crucial information in
detail; 2) the rating is not on an absolute scale for comparing to other items.
Because of these limitations, users cannot make right decision. In this paper,
we introduce our sophisticated approach to extract useful information from user
reviews using collapsed dependencies and sentiment analysis. We propose an
interactive word cloud that can show grammatical relationships among words, explore
reviews efficiently, and display positivity or negativity on a sentence. In
addition, we introduce visualization for comparing multiple word clouds and
illustrate the usage through test cases.
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