TripAdvisor is one of the largest travel website companies which adopts the user content model with nearly 50 million monthly visitors and millions of business reviews. We aim to improve the search experience by learning fine-grained information about each business, namely the users' sentiments toward specific entities as expressed in their reviews. Our approach allows us to rank these sentiments and implement a novel search experience where results are sorted by sentimental intensity towards the item of interest. These search results can further be enriched by displaying other positively (or negatively) mentioned items to the users. In addition, we present results from clustering businesses based on reviews. Our experiments show that these clusters are potentially useful for further optimization of the search engine.