DC Field | Value | Language |
---|---|---|
dc.contributor.author | Žunić, Anastazia | en_US |
dc.contributor.author | Corcoran, Padraig | en_US |
dc.contributor.author | Spasić, Irena | en_US |
dc.date.accessioned | 2022-12-09T13:25:25Z | - |
dc.date.available | 2022-12-09T13:25:25Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 0933-3657 | - |
dc.identifier.uri | http://researchrepository.mi.sanu.ac.rs/handle/123456789/4932 | - |
dc.description.abstract | Aspect-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.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Artificial Intelligence in Medicine | en_US |
dc.subject | Dependency parsing | Graph convolutional network | Natural language processing | Neural network | Sentiment analysis | en_US |
dc.title | Aspect-based sentiment analysis with graph convolution over syntactic dependencies | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.artmed.2021.102138 | - |
dc.identifier.pmid | 34531007 | - |
dc.identifier.scopus | 2-s2.0-85112331602 | - |
dc.contributor.affiliation | Computer Science | en_US |
dc.contributor.affiliation | Mathematical Institute of the Serbian Academy of Sciences and Arts | en_US |
dc.relation.firstpage | 102138 | - |
dc.relation.volume | 119 | - |
dc.description.rank | M21 | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
crisitem.author.orcid | 0000-0001-5222-1268 | - |
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