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.

基于方面的情感分析是一项自然语言处理任务，其目的是自动对与书面文本特定方面相关的情感进行分类。在这项研究中，我们提出了一种基于方面的情感分析的新模型，该模型利用图卷积利用句子的依赖解析树对给定方面的情感进行分类。为了在健康和福祉领域评估该模型，该任务偏向于负面情绪，我们使用了药物评论语料库。具体方面以统一医学语言系统为基础，这是一个相互关联的生物医学概念和相应术语的大型存储库。我们的实验表明，图卷积方法在基于方面的情感分析任务上优于标准深度学习架构。而且，依赖解析树上的图卷积（F-score 0.8179）在句子的平面序列表示（F-score 0.7332）上优于相同的方法。这些结果使情感分析在健康和幸福方面的性能与其他领域的最新技术水平保持一致。