Name
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 aspectbased 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.artmed.2021.102138 | ARTIFICIAL INTELLIGENCE IN MEDICINE |
Keywords | DocType | Volume |
Sentiment analysis, Natural language processing, Dependency parsing, Neural network, Graph convolutional network | Journal | 119 |
ISSN | Citations | PageRank |
0933-3657 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anastazia Žunić | 1 | 0 | 0.34 |
| Padraig Corcoran | 2 | 191 | 23.08 |
| Irena Spasić | 3 | 354 | 32.55 |