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Title: Sentiment analysis of patient feedback
Author: Smith, Phillip
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2017
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The application of sentiment analysis as a method for the automatic categorisation of opinions in text has grown increasingly popular across a number of domains over the past few years. In particular, health services have started to consider sentiment analysis as a solution for the task of processing the ever-growing amount of feedback that is received in regards to patient care. However, the domain is relatively under-studied in regards to the application of the technology, and the effectiveness and performance of methods have not been substantially demonstrated. Beginning with a survey of sentiment analysis and an examination of the work undertaken so far in the clinical domain, this thesis examines the application of supervised machine learning models to the classification of sentiment in patient feedback. As a starting point, this requires a suitably annotated patient feedback dataset, for both analysis and experimentation. Following the construction and detailed analysis of such a resource, a series of machine learning experiments study the impact of different models, features and review types to the problem. These experiments examine the applicability of the selected methods and demonstrate that model and feature choice may not be a significant issue in sentiment classification, whereas the type of review that the models train and test across does affect the outcome of classification. Finally, by examining the role that responses play in the patient feedback process and developing the idea of incorporating the inter-document context provided by the response into the feedback classification process, a recalibration framework for the labelling of sentiment in ambiguous texts for patient feedback is developed. As this detailed analysis will demonstrate, while some problems in performance remain despite the development and implementation of the recalibration framework, sentiment analysis of patient feedback is indeed viable, and achieves a classification accuracy of 91.4% and F1 of 0.902 on the gathered data. Furthermore, the models and data can serve as a baseline to study the nature of patient feedback, and provide a unique opportunity for the development of sentiment analysis in the clinical domain.
Supervisor: Not available Sponsor: Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: PE English ; QA75 Electronic computers. Computer science ; QA76 Computer software