A Novel Feature Cloud Visualization for Depiction of Product Features Extracted from Customer Reviews

Abstract

There has been an exponential growth of web content on the World Wide Web and online users contributing to majority of the unstructured data which also contain a good amount of information on many different subjects that may range from products, news, programmes and services. Many a times other users read these reviews and try to find the meaning of the sentences expressed by the reviewers. Since the number and the length of the reviews are so large that most the times the user will read a few reviews and would like to take an informed decision on the subject that is being talked about. Many different methods have been adopted by websites like numerical rating, star rating, percentage rating etc. However, these methods fail to give information on the explicit features of the product and their overall weight when taking the product in totality. In this paper, a framework has been presented which first calculates the weight of the features depending on the user satisfaction or dissatisfaction expressed on individual features and further a feature cloud visualization has been proposed which uses two level of specificity where the first level lists the extracted features and the second level shows the opinions on those features. A font generation function has been applied which calculates the font size depending on the importance of the features vis-a-vis with the opinion expressed on them.