Authors: Žunić, Anastazia 
Corcoran, Padraig
Spasić, Irena
Affiliations: Computer Science 
Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Aspect-based sentiment analysis with graph convolution over syntactic dependencies
Journal: Artificial Intelligence in Medicine
Volume: 119
First page: 102138
Issue Date: 2021
Rank: M21
ISSN: 0933-3657
DOI: 10.1016/j.artmed.2021.102138
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.
Keywords: Dependency parsing | Graph convolutional network | Natural language processing | Neural network | Sentiment analysis
Publisher: Elsevier

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