DC FieldValueLanguage
dc.contributor.authorŽunić, Anastaziaen_US
dc.contributor.authorCorcoran, Padraigen_US
dc.contributor.authorSpasić, Irenaen_US
dc.date.accessioned2022-12-09T13:25:25Z-
dc.date.available2022-12-09T13:25:25Z-
dc.date.issued2021-
dc.identifier.issn0933-3657-
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/4932-
dc.description.abstractAspect-based sentiment analysis is a natural language processing task whose aim is to automatically classify the sentiment associated with a specific aspect of a written text. In this study, we propose a novel model for aspect-based sentiment analysis, which exploits the dependency parse tree of a sentence using graph convolution to classify the sentiment of a given aspect. To evaluate this model in the domain of health and well-being, where this task is biased toward negative sentiment, we used a corpus of drug reviews. Specific aspects were grounded in the Unified Medical Language System, a large repository of inter-related biomedical concepts and the corresponding terminology. Our experiments demonstrated that graph convolution approach outperforms standard deep learning architectures on the task of aspect-based sentiment analysis. Moreover, graph convolution over dependency parse trees (F-score of 0.8179) outperforms the same approach over a flat sequence representation of sentences (F-score of 0.7332). These results bring the performance of sentiment analysis in health and well-being in line with the state of the art in other domains.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofArtificial Intelligence in Medicineen_US
dc.subjectDependency parsing | Graph convolutional network | Natural language processing | Neural network | Sentiment analysisen_US
dc.titleAspect-based sentiment analysis with graph convolution over syntactic dependenciesen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.artmed.2021.102138-
dc.identifier.pmid34531007-
dc.identifier.scopus2-s2.0-85112331602-
dc.contributor.affiliationComputer Scienceen_US
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Artsen_US
dc.relation.firstpage102138-
dc.relation.volume119-
dc.description.rankM21-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.orcid0000-0001-5222-1268-
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