RAPOT: An Adaptive Multifactor Risk Assessment Framework on Public Opinion for Trial Management

摘要

Since public opinion from social media has a growing impact and supervision on trial, risk assessment on public opinion is increasingly important in refined trial management. However, the tremendous amount of public opinion and the insufficient historical logs of trial procedures bring challenges to risk assessment on public opinion. To address this, we propose an adaptive multifactor risk assessment framework on public opinion with fuzzy numbers. Initially, we establish a multilayer indicator model for assessing the risk of public opinion (POR) with multilayer analysis and decision methods. Then, we explore the association rules hidden in the process logs to update the indicator model periodically. Moreover, we design a public opinion analysis module for indicator evaluation, including analysis in public opinion sentiment, hot search, and social media coverage to deal with big data on social media. Especially, the public opinion sentiment is classified by topic-based BiLSTM (T-BiLSTM), which is more accurate. Finally, the fuzzy number similarity is employed to determine POR’s level in the nine-level risk system. Experimental results validate the efficiency of our framework when assessing the POR.