The Generation of a Corpus for Clinical Sentiment Analysis

Clinical care providers express their judgments and observations towards the patient status in clinical narratives. In contrast to sentiment expressions in general domains targeted by language technology, clinical sentiments are influenced by related medical events such as clinical precondition or outcome of a treatment. We argue that patient status in terms of positive, negative and neutral judgements can only suboptimally be judged with generic approaches, and requires specific resources in term of a lexicon and training corpus targeting clinical sentiment. To address this challenge, we manually developed a corpus based on 300 ICU nurse letters derived from a clinical database, and an annotation scheme for clinical sentiment. The paper discusses influence patterns between clinical context and clinical sentiments as well as a semi-automatic method to generate a larger annotated corpus.

[1]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[2]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[3]  Swapna Somasundaran,et al.  Recognizing Stances in Online Debates , 2009, ACL.

[4]  Manfred Klenner,et al.  Sentiframes: A Resource for Verb-centered German Sentiment Inference , 2016, LREC.

[5]  Thierry Declerck,et al.  SentiMerge: Combining Sentiment Lexicons in a Bayesian Framework , 2014, LG-LP@COLING.

[6]  Marilyn A. Walker,et al.  Collective Stance Classification of Posts in Online Debate Forums , 2014 .

[7]  Ulf Leser,et al.  SCARE ― The Sentiment Corpus of App Reviews with Fine-grained Annotations in German , 2016, LREC.

[8]  Uladzimir Sidarenka PotTS: The Potsdam Twitter Sentiment Corpus , 2016, LREC.

[9]  Fabrice Lefèvre,et al.  Automatic Corpus Extension for Data-driven Natural Language Generation , 2016, LREC.

[10]  Mike Wells,et al.  Structured Models for Fine-to-Coarse Sentiment Analysis , 2007, ACL.

[11]  Janyce Wiebe,et al.  MPQA 3.0: An Entity/Event-Level Sentiment Corpus , 2015, NAACL.

[12]  Christopher S. G. Khoo,et al.  Sentiment lexicons for health-related opinion mining , 2012, IHI '12.

[13]  Yihan Deng,et al.  Retrieving Attitudes: Sentiment Analysis from Clinical Narratives , 2014, MedIR@SIGIR.

[14]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[15]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[16]  Yihan Deng,et al.  Sentiment analysis in medical settings: New opportunities and challenges , 2015, Artif. Intell. Medicine.