Assisted Dying: A Text Analysis of Public Sentiment and Opinion

Assisted dying has become increasingly acute in ethical, social, and political debates in current society. In this paper, Natural Language Processing (NLP) is used to analyze public opinions, sentiment, emotion and opinion on assist-ed dying. This study was performed on the public discourse dataset Search_All_LinksData 2014-2025, which contains 497 cleaned textual records of a decade. Data Collection and Pre-processing included Tokenization, Lemmatization, and Stop-word removal. For polarity measurement and con-textual interpretation, Text Blob and a fine-tuned BERT model was utilized. The results indicate that public opinion is polarized with 27.4% neutral, 38.2% positive, and 34.4% negative sentiment. Assisted dying related online discussions are inspired by autonomy, dignity, moral uncertainty and ideas of compassion. Overall, the study how NLP provides the means to detect emotional and moral dimensions within healthcare-related debates, to bring insights for improving the research and framing policies.