Mixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback
This work addresses data scarcity in public health sentiment analysis, but it is incremental as it applies existing methods to a new domain.
The paper tackled cross-domain sentiment classification of patient feedback by leveraging general-domain reviews to address data scarcity, achieving competitive performance on a new benchmark with four polarity classes.
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.