Numerous reviews are available online regarding a wide range of products and services. Aspect-Based Sentiment Analysis aims at extracting sentiment polarity per aspect instead of only the whole product or service. In this work, we use restaurant data from Task 5 of SemEval 2016 to investigate the potential of ontologies to improve the aspect sentiment classification produced by a support vector machine. We achieve this by combining a standard bag-of-words model with external dictionaries and an ontology. Our ontology-enhanced methods yield significantly better performance compared to the methods without ontology features: we obtain a significantly higher F1 score and require less than 60% of the training data for equal performance.

doi.org/10.1007/978-3-319-68786-5_27, hdl.handle.net/1765/102435
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Erasmus University Rotterdam

de Heij, D., Troyanovsky, A., Yang, C., Zychlinsky Scharff, M., Schouten, K., & Frasincar, F. (2017). An ontology-enhanced hybrid approach to aspect-based sentiment analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). doi:10.1007/978-3-319-68786-5_27