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Aspect-based sentiment analysis with graph convolution over syntactic dependencies

Zunic, Anastazia, Corcoran, Padraig and Spasic, Irena 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: Computer Science & Informatics
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: 07 Oct 2021 17:32
URI: http://orca.cardiff.ac.uk/id/eprint/143246

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