In this paper, a semi-automatic approach for building a sentiment domain ontology is proposed. Differently than other methods, this research makes use of synsets in term extraction, concept formation, and concept subsumption. Using several state-of-the-art hybrid aspect-based sentiment analysis methods like Ont + CABASC and Ont + LCR-Rot-hop on a standard dataset, the accuracies obtained using the semi-automatically built ontology as compared to the manually built one, are slightly lower (from approximately 87% to 84%). However, the user time needed for building the ontology is reduced by more than half (from 7 h to 3 h), thus showing the usefulness of this work. This is particularly useful for domains for which sentiment ontologies are not yet available.

Additional Metadata
Keywords Aspect-based sentiment analysis, Semi-automatic ontology building, Sentiment domain ontology
Persistent URL dx.doi.org/10.1007/978-3-030-49461-2_7, hdl.handle.net/1765/127802
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Citation
Dera, E. (Ewelina), Frasincar, F, Schouten, K.I.M, & Zhuang, L. (Lisa). (2020). SASOBUS: Semi-automatic Sentiment Domain Ontology Building Using Synsets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). doi:10.1007/978-3-030-49461-2_7