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A Corpus-Driven Approach to Sentiment Analysis of Patient Narratives

15 pagesPublished: November 29, 2016

Abstract

This paper describes the linguistic analysis of a corpus of patient narratives that was used to develop and test software to carry out sentiment analysis on the aforementioned corpus. There is a growing body of research on the relationship between sentiment analysis, social media (for example, Twitter) and health care, but less research on sentiment analysis of patient narratives (being longer and more complex texts). The motivation for this research is that patient narratives of experiences of the National Health Service (NHS) in the UK provide rich data of the treatment received.
The corpus threw up some unexpected results that may be of benefit for researchers of sentiment analysis. The linguistic problems encountered have been divided into three sections: the noisy nature of large corpora; the idiomatic nature of language; the nature of language in the clinical domain. This article gives an overview of the project and describes the linguistic problems that arose out of the project, which tried to find a means to automate the analysis of patient feedback on health services.

Keyphrases: clinical domain, corpus linguistics, patient narratives, Sentiment Analysis

In: Antonio Moreno Ortiz and Chantal Pérez-Hernández (editors). CILC2016. 8th International Conference on Corpus Linguistics, vol 1, pages 381--395

Links:
BibTeX entry
@inproceedings{CILC2016:Corpus_Driven_Approach_to_Sentiment,
  author    = {Keith Stuart and Ana Botella and Imma Ferri-Miralles},
  title     = {A Corpus-Driven Approach to Sentiment Analysis of Patient Narratives},
  booktitle = {CILC2016. 8th International Conference on Corpus Linguistics},
  editor    = {Antonio Moreno Ortiz and Chantal P\textbackslash{}'erez-Hern\textbackslash{}'andez},
  series    = {EPiC Series in Language and Linguistics},
  volume    = {1},
  pages     = {381--395},
  year      = {2016},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5283},
  url       = {https://easychair.org/publications/paper/r8SR},
  doi       = {10.29007/rs9b}}
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