Online Review Analysis by Visual Feature Selection

Abstract

The purpose of this work is to show that feature selection through visualization is at least as powerful as the best automatic feature selection algorithms. This is proved by applying our visualization technique on the online review analysis. Opinion spamming is a reality, and it can have unpleasant consequences in the retail industry. While there are, several promising research works done on identifying the fake online reviews from genuine online reviews, there have been few involving visualization and visual analytics. This is achieved by applying radial chart visualization technique to the online review classification into fake and genuine reviews. Radial chart and the color overlaps are used to explore the best feature selection through visualization for classification. The system gives a structure to each text review based on certain attributes, compares how different or similar the structure of the different or same categories are, and highlights the key features that contribute to the classification the most. Our visualization technique helps the user get insights into the high dimensional data by providing means to eliminate the worst features right away, pick some best features without statistical aids, understand the behavior of the dimensions in different combinations.