The new social and technological framework of Web 2.0 has resulted in the availability of significant volumes of user generated content. In some cases, user generated content describes existing services, as in the case of travel reviews, in which the users express their experiences and opinions about hotels among other aspects of travel experience. Existing approaches to measuring hotel quality from the customer perspective usually follow the expectation-experience gap model of SERVQUAL or some form of incident analysis. However, user generated content can be used as a complement to automatically gather user opinions in which the aspects covered are those spontaneously raised by customers. This paper reports an initial exploration of such approach on a small sample or reviews in Spanish gathered from TripAdvisor, using existing classifications of emotion types and eliciting conditions. Shallow natural language processing (NLP) techniques are applied to automatically extract simple expressions that can be used to obtain a profile of hotel quality. The results of the preliminary study were able to identify emotion types and eliciting conditions with a reasonable effectiveness which points out to the potential of the techniques to become a complementary tool for hotel evaluation.