This paper describes a new dataset for aspect-based sentiment analysis (ABSA) for analyzing patient feedback about healthcare services. In an interdisciplinary collaboration spanning the fields of natural language processing and healthcare research, we manually annotate a dataset of 2382 free-text comments collected from national patient experience surveys in Norway, covering two sub-fields of services – special mental healthcare and general practitioners. Annotations are provided on both the sentence- and comment-level, covering a fine-grained set of 25 unique healthcare-related aspects and their polarities. We also report results for fine-tuning both encoder- and decoder models on the resulting dataset, comparing different modeling strategies, like joint and sequential prediction of aspects and polarity. The resources developed in this work can assist healthcare researchers in the analysis of patient feedback, bringing a much more efficient approach compared to today’s manual analysis, potentially leading to improved patient satisfaction and clinical outcomes.

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.

We present EDEN, the first Norwegian dataset annotated with event information at the sentence level, adapting the widely used ACE event schema to Norwegian. The paper describes the manual annotation of Norwegian text as well as transcribed speech in the news domain, together with inter-annotator agreement and discussions of relevant dataset statistics. We also present preliminary modeling results using a graph-based event parser. The resulting dataset will be freely available for download and use.