Aspect-based sentiment analysis with graph convolution over syntactic dependencies

Zunic, Anastazia, Corcoran, Padraig ORCID: https://orcid.org/0000-0001-9731-3385 and Spasic, Irena ORCID: https://orcid.org/0000-0002-8132-3885 2021. Aspect-based sentiment analysis with graph convolution over syntactic dependencies. Artificial Intelligence in Medicine 119 , 102138. 10.1016/j.artmed.2021.102138

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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 towards 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. Keywords: sentiment analysis, natural language processing, dependency parsing, neural network, graph convolutional network.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics Research Institutes & Centres > Data Innovation Research Institute (DIURI)
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Publisher:	Elsevier
ISSN:	0933-3657
Date of First Compliant Deposit:	10 September 2021
Date of Acceptance:	3 August 2021
Last Modified:	12 Nov 2024 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/143246

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