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European Semantic Web Conference

ESWC 2022: The Semantic Web pp 183–199Cite as

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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Authors and Affiliations

  1. Erasmus University Rotterdam, PO Box 1738, 3000 DR, Rotterdam, The Netherlands

    Roos van Lookeren Campagne, David van Ommen, Mark Rademaker, Tom Teurlings & Flavius Frasincar

Authors
  1. Roos van Lookeren Campagne
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  2. David van Ommen
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  3. Mark Rademaker
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  4. Tom Teurlings
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  5. Flavius Frasincar
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Corresponding author

Correspondence to Flavius Frasincar .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Paul Groth

  2. Universidad Simón Bolívar, Leibniz Information Centre for Science and Technology, Hannover, Niedersachsen, Germany

    Maria-Esther Vidal

  3. Institut Polytechnique de Paris "DIG", Télécom ParisTech, Palaiseau, France

    Fabian Suchanek

  4. University of Southern California, Marina del Rey, CA, USA

    Pedro Szekley

  5. IBM Research - Thomas J. Watson Research, Yorktown Heights, NY, USA

    Pavan Kapanipathi

  6. LaSIGE, Fac de Ciencias,Edif C6, Pis0 3, Universidade de Lisboa, Lisbon, Portugal

    Dr. Catia Pesquita

  7. University of Nantes, Nantes, France

    Hala Skaf-Molli

  8. Aalto University, Espoo, Finland

    Minna Tamper

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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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