Abstract
Since the increase of Web reviews of products and services, Aspect-Based Sentiment Classification (ABSC) has become more important to determine the sentiment of online opinions. Useful information extracted from these reviews can then be used by companies themselves, but can also be applicable by consumers. In the recent literature on ABSC, hybrid methods, which combine knowledge-based and machine learning approaches, are becoming more popular as well. However, in this work, instead of following a two-step procedure, we attempt to improve the model accuracy by proposing to directly inject the information from a domain ontology in a state-of-the-art neural network model, more precisely LCR-Rot-hop++. Furthermore, by using soft-positioning and visible matrices we aim to prevent that the injected knowledge hinders the semantics of the original sentences. To evaluate the accuracy of our model, LCR-Rot-hop-ont++, we use the standard SemEval 2015 and SemEval 2016 datasets for ABSC. We conclude that knowledge injection in the neural network is effective for sentiment classification, especially if the amount of labeled data is limited.
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Dekker, R., Gielisse, D., Jaggan, C., Meijers, S., Frasincar, F. (2023). Knowledge Injection for Aspect-Based Sentiment Classification. In: Strauss, C., Amagasa, T., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2023. Lecture Notes in Computer Science, vol 14147. Springer, Cham. https://doi.org/10.1007/978-3-031-39821-6_14
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