跨领域分析患者反馈情感，提升医疗服务质量评估
Mixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback
- 用通用领域评论数据缓解医疗文本标注稀缺问题
- 在多个模型架构上验证跨域效果，发现混合情感识别挑战大
- 适合医疗决策支持与自然语言处理研究者参考
对公众健康领域患者反馈进行情感分析，有助于决策者评估医疗服务。本文聚焦于全科医生和精神卫生服务的患者调查中自由文本评论的情感分析，采用四种句级极性类别（正面、负面、混合、中性）进行标注，并通过利用通用领域评论数据缓解标注数据稀缺问题。针对多种模型架构，比较了领域内与跨领域训练的效果，以及联合多领域模型训练的影响。
原文摘要 · Abstract (English)
Sentiment analysis of patient feedback from the public health domain can aid decision makers in evaluating the provided services. The current paper focuses on free-text comments in patient surveys about general practitioners and psychiatric healthcare, annotated with four sentence-level polarity classes -- positive, negative, mixed and neutral -- while also attempting to alleviate data scarcity by leveraging general-domain sources in the form of reviews. For several different architectures, we compare in-domain and out-of-domain effects, as well as the effects of training joint multi-domain models.
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