Analysing Public Perception of South Korea's Low Birth Rate Policies Using NLP-based Sentiment Analysis

Korea's low birth rate has become a significant demographic problem with far-reaching socioeconomic implications. Using YouTube comments as a primary data source, this study begins a complex exploration of public sentiment on Korea's low birth rate policy. Compare and analyze the effect of emotion analysis by applying various NLP (Natural Language Processing) models. Findings reveal the complexity and pros and cons of model selection, low fertility's socioeconomic impact, and public opinion's role in shaping policy. This study also explores challenges such as data imbalance, decoding complex language structures, and managing computational requirements. This study concludes with insights into the broader policy implications of the NLP model, future directions for NLP-based public policy research, and the ethical considerations that need to be addressed. This research contributes to a more responsive, comprehensive, and adequate understanding of public sentiment and policies by laying the groundwork for a more nuanced understanding.