2017-10-04
An ontology-enhanced hybrid approach to aspect-based sentiment analysis
Publication
Publication
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.
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| 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) | |
| Organisation | Erasmus University Rotterdam |
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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 |
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