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 ... 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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