Name
Abstract | ||
|---|---|---|
Online reviews are an important asset for users deciding to buy a product, see a movie, or go to a restaurant, as well as for busi- nesses tracking user feedback. However, most reviews are written in a free-text format, and are therefore difficult for computer sys- tems to understand, analyze, and aggregate. One consequence of this lack of structure is that searching text reviews is often frus- trating for users. User experience would be greatly improved if the structure and sentiment conveyed in the content of the reviews were taken into account. Our work focuses on identifying this in- formation from free-form text reviews, and using the knowledge to improve user experience in accessing reviews. Specifically, we focused on improving recommendation accuracy in a restaurant re- view scenario. In this paper, we report on our classification effort, and on the insight on user-reviewing behavior that we gained in the process. We propose new ad-hoc and regression-based recommen- dation measures, that both take into account the textual component of user reviews. Our results show that using textual information re- sults in better general or personalized review score predictions than those derived from the numerical star ratings given by the users. |
Year | Venue | Keywords |
|---|---|---|
2009 | WebDB | user experience |
Field | DocType | Citations |
Data mining,User experience design,World Wide Web,Computer science,Textual information,Database,Goto | Conference | 124 |
PageRank | References | Authors |
5.57 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gayatree Ganu | 1 | 176 | 7.63 |
| Noemie Elhadad | 2 | 1131 | 69.59 |
| Amélie Marian | 3 | 1280 | 77.92 |