| dc.description.abstract | This thesis focuses on implementing text mining techniques, to analyse public comments on Dutch
healthcare policies. With the emergence of COVID-19, there was a significant increase in public
anxiety and uncertainty, leading to a surge in data from various communication channels. This
research consists of two parts, identifying questions, doubts, and concerns within these comments
using text classification and identifying topics using topic modelling approaches. The study evaluates
the effectiveness of different topic modelling techniques like Latent Dirichlet Allocation (LDA) and
BERTopic. To add to it, classification methods, including a rule-based approach, Naive Bayes, logistic
regression, and DistilBERT are also implemented. The findings showed that advanced models like
BERTopic and DistilBERT provide more nuanced and accurate insights into public sentiment, thereby
aiding policymakers in responding effectively to public feedback. This research has broader
implications for enhancing public health communication strategies and can benefit other
governmental institutions globally. | |
