Sentiment Analysis Using a Large Language Model–Based Approach to Detect Opioids Mixed With Other Substances Via Social Media: Method Development and Validation

This study demonstrates the potential of leveraging machine learning and large language models, such as GPT-3.5 Turbo, to analyze public sentiment surrounding opioid use and its associated risks. By using YouTube comments as a rich source of self-reported data, the study provides valuable insights into both the positive and negative effects of opioids, particularly when mixed with other substances. The proposed methodology significantly outperformed traditional models, contributing to more accurate predictions of overdose risks and enhancing health care responses to the opioid crisis.