WEB-SOBA: Word Embeddings-Based Semi-automatic Ontology Building for Aspect-Based Sentiment ClassificationDownload PDF

Dec 09, 2020 (edited Mar 16, 2021)ESWC 2021 ResearchReaders: Everyone
  • Keywords: ontology learning, word embeddings, sentiment analysis, aspect-based sentiment analysis
  • Abstract: For aspect-based sentiment analysis (ABSA), hybrid models combining ontology reasoning and machine learning approaches have achieved state-of-the-art results. In this paper, we introduce WEB-SOBA: a methodology to build a domain sentiment ontology in a semi-automatic manner from a domain-specific corpus using word embeddings. We evaluate the performance of a resulting ontology with a state-of-the-art hybrid ABSA framework, HAABSA, on the SemEval-2016 restaurant dataset. The performance is compared to a manually constructed ontology, and two other recent semi-automatically built ontologies. We show that WEB-SOBA is able to produce an ontology that achieves higher accuracy whilst requiring less than half of user time, compared to the previous approaches.
  • First Author Is Student: Yes
  • Subtrack: NLP and Information Retrieval
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