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

Fenna ten Haaf, Christopher Claassen, Ruben Eschauzier, Joanne Tjan, Daniël Buijs, Flavius Frasincar*, Kim Schouten

*Corresponding author for this work

Research output: Chapter/Conference proceedingChapterAcademic

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationThe semantic web
Subtitle of host publication18th Extended Semantic Web Conference (ESWC 2021)
EditorsRuben Verborgh, Katja Hose, Heiko Paulheim, Pierre-Antoine Champin, Maria Maleshkova, Oscar Corcho, Petar Ristoski, Mehwish Alam
PublisherSpringer-Verlag
Pages340-355
Number of pages16
Volume125
ISBN (Print)9783030773847
DOIs
Publication statusE-pub ahead of print - 31 May 2021
Event18th European Semantic Web Conference, ESWC 2021 - Virtual, Online
Duration: 6 Jun 202110 Jun 2021

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12731 LNCS
ISSN0302-9743

Conference

Conference18th European Semantic Web Conference, ESWC 2021
CityVirtual, Online
Period6/06/2110/06/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Research programs

  • ESE - E&MS

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