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Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Development of an Engine for Topic-Based Sentiment Analysis and Its Integration within the App ‘Digital Companion’ |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Adherence to therapy is a significant issue when treating patients who suffer from adiposity. To improve therapy success, modern technology can be used to assist in increasing patients' adherence to therapy. One such technology is the Digital Companion application, which includes a mobile app for patients to track their therapy progress and a web interface for doctors to access further analytics on their patients' ongoing therapy outcomes. This bachelor thesis investigates how we can contribute to the Digital Companion application by developing a Natural Language Processing engine that analyzes the journal entries written by patients in the Digital Companion mobile application. The analysis we provide consists of a topic-based sentiment analysis-oriented algorithm. Through this approach, our aim is to identify which aspects of the therapy are going well for the patient and which are not. This may not always be apparent to the doctor due to their limited time and resources when preparing for a specific patient's consultation and their lack of oversight of what occurs between consultations. With our proposed model, we achieved better precision and recall scores than other industry-leading models when evaluating the patients' data, demonstrating the effectiveness of our approach for the task at hand. |
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