Understanding patient opinions expressed towards healthcare services in online platforms could allow healthcare professionals to respond to address patients' concerns in a timely manner. Extracting patient opinion towards various aspects of health services is closely related to aspect-based sentiment analysis (ABSA) in which we need to identify both opinion targets and target-specific opinion expressions. The lack of aspect-level annotations however makes it difficult to build such an ABSA system. This paper proposes a joint learning framework for simultaneous unsupervised aspect extraction at the sentence level and supervised sentiment classification at the document level. It achieves 98.2% sentiment classification accuracy when tested on the reviews about healthcare services collected from Yelp, outperforming several strong baselines. Moreover, our model can extract coherent aspects and can automatically infer the distribution of aspects under different polarities without requiring aspect-level annotations for model learning.

了解患者对在线平台上医疗服务的意见可以让医疗保健专业人员及时做出回应，解决患者的担忧。提取患者对医疗服务各个方面的意见与基于方面的情感分析 (ABSA) 密切相关，其中我们需要识别意见目标和特定目标的意见表达。然而，缺乏方面级别的注释使得构建这样的 ABSA 系统变得困难。本文提出了一种联合学习框架，用于在句子级别同时进行无监督方面提取和在文档级别进行有监督的情感分类。在对从 Yelp 收集的有关医疗保健服务的评论进行测试时，它实现了 98.2% 的情感分类准确率，优于几个强大的基线。