An ontology-enhanced hybrid approach to aspect-based sentiment analysis
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.
|Persistent URL||dx.doi.org/10.1007/978-3-319-68786-5_27, hdl.handle.net/1765/102435|
|Series||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
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