This research explores the possibility of improving knowledge-driven aspect-based sentiment analysis (ABSA) in terms of efficiency and effectiveness. This is done by implementing a Semi-automated Ontology Builder for Aspect-based sentiment analysis (SOBA). Semi-automatization of the ontology building process could produce more extensive ontologies, whilst shortening the building time. Furthermore, SOBA aims to improve the effectiveness of its ontologies in ABSA by attaching to concepts the semantics provided by a semantic lexicon. To evaluate the performance of SOBA, ontologies are created using the ontology builder for the restaurant and laptop domains. The use of these ontologies is then compared with the use of manually constructed ontologies in a state-of-the-art knowledge-driven ABSA model, the Two-Stage Hybrid Model (TSHM). The results show that it is difficult for a machine to beat the quality of a human made ontology, as SOBA does not improve the effectiveness of TSHM, achieving similar results. Including the semantics provided by a semantic lexicon in general increases the performance of TSHM, albeit not significantly. However, SOBA decreases by 50% or more the human time needed to build ontologies, so that it is recommended to use SOBA for knowledge-driven ABSA frameworks, as it leads to greater efficiency.

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Journal of Web Semantics
Erasmus University Rotterdam

Zhuang, L. (Lisa), Schouten, K., & Frasincar, F. (2020). SOBA: Semi-automated Ontology Builder for Aspect-based sentiment analysis. Journal of Web Semantics. doi:10.1016/j.websem.2019.100544