Clustered Layout Word Cloud for User Generated Online Reviews

Abstract

User generated reviews, like those found on Yelp and Amazon, have become important reference material in casual decision making, like dining, shopping and entertainment. However, very large amounts of reviews make the review reading process time consuming. A text visualization can speed up the review reading process.

In this thesis, we present the clustered layout word cloud -- a text visualization that quickens decision making based on user generated reviews. We used a natural language processing approach, called grammatical dependency parsing, to analyze user generated review content and create a semantic graph. A force-directed graph layout was applied to the graph to create the clustered layout word cloud.

We conducted a two-task user study to compare the clustered layout word cloud to two alternative review reading techniques: random layout word cloud and normal block-text reviews. The results showed that the clustered layout word cloud offers faster task completion time and better user satisfaction than the other two alternative review reading techniques.

[Permission email from J. Huang removed at his request. GMc March 11, 2014]