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

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

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.

Subtrack: NLP and Information Retrieval

First Author Is Student: Yes