• DocumentCode
    2030568
  • Title

    Extracting sentiment from healthcare survey data: An evaluation of sentiment analysis tools

  • Author

    Georgiou, Despo ; MacFarlane, Andrew ; Russell-Rose, Tony

  • Author_Institution
    Dept. of Comput. Sci., City Univ. London, London, UK
  • fYear
    2015
  • fDate
    28-30 July 2015
  • Firstpage
    352
  • Lastpage
    361
  • Abstract
    Sentiment analysis is an emerging discipline with many analytical tools available. This project aimed to examine a number of tools regarding their suitability for healthcare data. A comparison between commercial and non-commercial tools was made using responses from an online survey which evaluated design changes made to a clinical information service. The commercial tools were Semantria and TheySay and the noncommercial tools were WEKA and Google Prediction API. Different approaches were followed for each tool to determine the polarity of each response (i.e. positive, negative or neutral). Overall, the non-commercial tools outperformed their commercial counterparts. However, due to the different features offered by the tools, specific recommendations are made for each. In addition, single-sentence responses were tested in isolation to determine the extent to which they more clearly express a single polarity. Further work can be done to establish the relationship between single-sentence responses and the sentiment they express.
  • Keywords
    Internet; application program interfaces; health care; learning (artificial intelligence); Google Prediction API; WEKA; clinical information service; commercial tool; healthcare data; healthcare survey data; online survey; sentiment analysis tool; single-sentence response; Accuracy; Google; Medical services; Sentiment analysis; Supervised learning; Testing; Training; classification; healthcare; machine learning; sentiment analysis; tools;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Science and Information Conference (SAI), 2015
  • Conference_Location
    London
  • Type

    conf

  • DOI
    10.1109/SAI.2015.7237168
  • Filename
    7237168