Review-level aspect-based sentiment analysis using an ontology

Abstract

The rapid growth of the World Wide Web has led to an explosion of information that is available on this platform. This has resulted in an increased interest in sentiment analysis, where the goal is to determine the opinion regarding a topic. Aspect-based sentiment analysis aims to capture the sentiment within a segment of text for mentioned aspects, rather than for the text as a whole. The task we consider is aspect-based sentiment analysis at the review-level for restaurant reviews. We focus on ontology-enhanced methods that complement a standard machine learning algorithm. For this task we use two different algorithms, a review-based and a sentence aggregation algorithm. By using an ontology as a knowledge base, the classification performance of our models improves significantly. Furthermore, the review-based algorithm gives more accurate predictions than the sentence aggregation algorithm.