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DCWEB-SOBA: Deep Contextual Word Embeddings-Based Semi-automatic Ontology Building for Aspect-Based Sentiment Classification

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13261)

Abstract

In this paper, we propose the use of deep contextualised word embeddings to semi-automatically build a domain sentiment ontology. Compared to previous research, we use deep contextualised word embeddings to better cope with various meanings of words. A state-of-the-art hybrid method is used for aspect-based sentiment analysis, called HAABSA++, to evaluate our obtained ontology on the SemEval-2016 restaurant dataset. We achieve a prediction accuracy of 81.85% for the hybrid model with our ontology, which outperforms the hybrid model with other considered ontologies. Furthermore, we find that the ontology obtained from our proposed domain sentiment ontology builder, called DCWEB-SOBA, on itself improves the accuracy for the conclusive cases from 83.04% to 84.52% compared to the ontology builder based on non-contextual word embeddings, WEB-SOBA.

Keywords

  • Ontology learning
  • Contextual word embeddings
  • Aspect-based sentiment analysis
  • Hybrid method

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Correspondence to Flavius Frasincar .

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van Lookeren Campagne, R., van Ommen, D., Rademaker, M., Teurlings, T., Frasincar, F. (2022). DCWEB-SOBA: Deep Contextual Word Embeddings-Based Semi-automatic Ontology Building for Aspect-Based Sentiment Classification. In: , et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_11

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  • DOI: https://doi.org/10.1007/978-3-031-06981-9_11

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