Due to the increase in the number of people participating online on reviewing travel related entities such as hotels, cities and attractions, there is a rich corpus of textual information available online. However, to make a decision on a certain entity, one has to read many such reviews manually, which is inconvenient. To make sense of the reviews, the essential first step is to understand the semantics that lie therein. This paper discusses a system that uses machine learning based classifiers to label the entities found in text into semantic concepts defined in an ontology. A subject classifier with a precision of 0.785 and a sentiment classifier with a correlation coefficient of 0.9423 was developed providing sufficient accuracy for subject categorization and sentiment evaluation in the proposed system.