Online travel portals are becoming important parts for sharing travel information. User generated content and information in user reviews is valuable to both travel companies and to other people and can have a substantial impact on their decision making process. The automatic analysis of used generated reviews can provide a deeper understanding of users attitudes and opinions. In this paper, we present a work on the automatic analysis of user reviews on the booking.com portal and the automatic extraction and visualization of information. An aspect based approach is followed where latent dirichlet allocation is utilized in order to model topic opinion and natural language processing techniques are used to specify the dependencies on a sentence level and determine interactions between words and aspects. Then Naïve Bayes machine learning method is used to recognize the polarity of the user's opinion utilizing the sentence's dependency triples. To evaluate the performance of our method, we collected a wide set of reviews for a series of hotels from booking.com. The results from the evaluation study are very encouraging and indicate that the system is fast, scalable and most of all accurate in analyzing user reviews and in specifying users' opinions and stance towards the characteristics of the hotels and can provide comprehensive hotel information.
REF used latent dirichlet allocation to extract aspects and Naïve Bayes to recognize the polarity on customer reviews for the hotel domain.
A System for Aspect-based Opinion Mining of Hotel Reviews
WEBIST
Computer Science