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 that can be used to compare items. Because of these limitations, users cannot make an optimal 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.