Feature Based Opinion Mining for Hotel Profiling

Hotel profiling plays an important role in hotel recommendation. With the proliferation of huge amount of user-generated-reviews on web-sites, hotel profiling has become more challenging as these reviews and embedded opinions could indirectly drive hotels. Comprehensive hotel profiling based on review analysis could help people to get an overall opinion on hotels and hence to facilitate mindful tourism. To avoid deficiencies of many other recent researches, this research focuses more on the feature-based opining mining rather analysing only sentiments of the reviews. Thus, a semantic profiling approach which integrates a machine learning technique, part-of-speech (PoS) tagging and Ontology is proposed for feature-based hotel profiling. PoS tagging is used for recognising patterns of opinions and SentiWordNet is used to resolve semantic heterogeneity of the opinion phrases and to classify them. Feature-based analysis could generate the feature level opinion about a hotel in several aspects including food, hospitality and environment.