title: Sentiment analysis in medical settings: New opportunities and challenges.
abstract: OBJECTIVE
  Clinical documents reflect a patient's health status in terms of observations and contain objective information such as descriptions of examination results, diagnoses and interventions. To evaluate this information properly, assessing positive or negative clinical outcomes or judging the impact of a medical condition on patient's well being are essential. Although methods of sentiment analysis have been developed to address these tasks, they have not yet found broad application in the medical domain.
  
  
  METHODS
  In this work, we characterize the facets of sentiment in the medical sphere and identify potential use cases. Through a literature review, we summarize the state of the art in healthcare settings. To determine the linguistic peculiarities of sentiment in medical texts and to collect open research questions of sentiment analysis in medicine, we perform a quantitative assessment with respect to word usage and sentiment distribution of a dataset of clinical narratives and medical social media derived from six different sources.
  
  
  RESULTS
  Word usage in clinical narratives differs from that in medical social media: Nouns predominate. Even though adjectives are also frequently used, they mainly describe body locations. Between 12% and 15% of sentiment terms are determined in medical social media datasets when applying existing sentiment lexicons. In contrast, in clinical narratives only between 5% and 11% opinionated terms were identified. This proves the less subjective use of language in clinical narratives, requiring adaptations to existing methods for sentiment analysis.
  
  
  CONCLUSIONS
  Medical sentiment concerns the patient's health status, medical conditions and treatment. Its analysis and extraction from texts has multiple applications, even for clinical narratives that remained so far unconsidered. Given the varying usage and meanings of terms, sentiment analysis from medical documents requires a domain-specific sentiment source and complementary context-dependent features to be able to correctly interpret the implicit sentiment.
authors.affiliation: Innovation Center Computer Assisted Surgery, University of Leipzig, Semmelweisstr. 14, 04103 Leipzig, Germany. Electronic address: kerstin.denecke@iccas.de.
authors.forename: Kerstin
authors.initials: K
authors.lastname: Denecke
authors.affiliation: Innovation Center Computer Assisted Surgery, University of Leipzig, Semmelweisstr. 14, 04103 Leipzig, Germany.
authors.forename: Yihan
authors.initials: Y
authors.lastname: Deng
mesh_terms: D057225:Data Mining / Q000379:methods*; D006304:Health Status; D008490:Medical Informatics / Q000379:methods*; D009323:Natural Language Processing
keywords: Clinical text mining; Health status analysis; Medical language processing; Sentiment analysis