Paper Info
Title
Improving the quality of predictions using textual information in online user reviews
Abstract
Online reviews are often accessed by users deciding to buy a product, see a movie, or go to a restaurant. However, most reviews are written in a free-text format, usually with very scant structured metadata information and are therefore difficult for computers to understand, analyze, and aggregate. Users then face the daunting task of accessing and reading a large quantity of reviews to discover potentially useful information. We identified topical and sentiment information from free-form text reviews, and use this knowledge to improve user experience in accessing reviews. Specifically, we focus on improving recommendation accuracy in a restaurant review scenario. We propose methods to derive a text-based rating from the body of the reviews. We then group similar users together using soft clustering techniques based on the topics and sentiments that appear in the reviews. Our results show that using textual information results in better review score predictions than those derived from the coarse numerical star ratings given by the users. In addition, we use our techniques to make fine-grained predictions of user sentiments towards the individual topics covered in reviews with good accuracy.
Year
DOI
Venue
2013
10.1016/j.is.2012.03.001
Inf. Syst.
Keywords
DocType
Volume
group similar user,online review,sentiment information,accessing review,textual information result,online user review,better review score prediction,good accuracy,free-form text review,useful information,scant structured metadata information
Journal
38
Issue
ISSN
Citations 
1
0306-4379
46
PageRank 
References 
Authors
1.31
27
3
Name
Order
Citations
PageRank
Gayatree Ganu11767.63
Yogesh Kakodkar2461.31
Amélie Marian3128077.92