14:00, Room G008 (Parc Club)
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 businesses tracking user feedback. However, most reviews are written in a free-text format, and are therefore difficult for computer systems to understand, analyze, and aggregate. One consequence of this lack of structure is that searching text reviews is often frustrating for users; keyword searches typically do not provide good results as the same keywords routinely appear in good and in bad reviews. User experience would be greatly improved if the structure and sentiment information conveyed in the content of the reviews were taken into account. Our work focuses on identifying this structure and sentiment information from free-text reviews, and using this knowledge to improve user experience in accessing reviews. Specifically, we focused on improving recommendation accuracy in a restaurant review scenario.
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 recommendation measures, that both take into account the textual component of user reviews. Our results show that using textual information results in better general or personalized restaurant score predictions than those derived from the numerical star ratings given by the users.