Aspect-Based Restaurant Information Extraction for the Recommendation System

摘要

In this paper information extraction task for the restaurant recommendation system is considered. We develop an information extraction system which is intended to gather restaurants aspects from users' reviews and output them to the recommendation module. As many of the restaurant aspects are subjective, our task can also be called sentiment analysis, or opinion mining. Thus, we present an aspect-based approach towards sentiment analysis of reviews about restaurants for e-tourism recommender systems. The analyzed frames are service and food quality, cuisine, price level, noise level, etc. In this paper we focus on service quality, cuisine type and food quality. As part of the preprocessing phase, a method for Russian reviews corpus analysis (as part of information extraction) is proposed. Its importance is shown at the experimental phase, when the application of machine learning techniques to aspects extraction is analyzed. It is shown that the information obtained during corpus analysis improve system performance. We conduct experiments with several feature sets and classifiers and show that the use of resources learnt from the corpus leads to the improvement of the models. Naive Bayes appears to be the best choice for sentiment classification, while Logistic Regression and SVM are best at deciding on the relevance of a review with respect to the particular aspect.