Ⅰ. Background and Aims of Research
oWe continue to build up ‘Environmental Policy monitoring System’ dedicated to periodically identify environmental policy needs and assess timeliness and effectiveness of environmental policy as we did last year
ㅇ Environmental Policy monitoring System apply prediction accuracy and real-time data collection-analysis-diffusion capability of Machine learning to environmental policy research
ㅇOur ‘Environmental Policy monitoring System’ consists of three components: ‘Deep Learning Based Pollution Prediction algorithm’, ‘Real Time Environmental Text Analysis algorithm’, ‘Issue Based Database’
- Deep Learning Based Pollution algorithm: Periodically update various pollution prediction
-Real Time Environmental Text Analysis algorithm: Periodically summarise environment related text data and sentiment analysis
o Text summary: abstract keywords and keyword network from texts produced by environmental policy provider and environmental policy consumers
o Sentiment analysis: Real-time collection and sentiment analysis of SNS related to all subfield of environment
- Issue Based Database: Key environmental issue network connected with data analysis for each issue updating real-time

ㅇ Policy need Identification: Detect environment policy areas and regions in need of intervention from the predictions of ‘Deep Learning Based Pollution Prediction algorithm’, the text analysis results of ‘Real Time Environmental Text Analysis algorithm’, and the data analysis results of ‘Issue Based Database’
ㅇ Timeliness assessment: check if the temporal pattern of keywo