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
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

Show full item record


checked on Jun 14, 2024

Page view(s)

checked on May 9, 2024

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.