Optimization of Event Evolutionary Graph Method for Complex Online Public Opinion Knowledge Discovery
[Research purpose]Optimizing the event evolutionary graph construction method can enhance the knowledge discovery ability of the event evolutionary graph tool in unstructured online public opinion data,and it can better explore the causal relationship and evolu-tionary path within complex online public opinion events.[Research method]The study adopts RoBERTa pre-training model for se-quence labeling to replace the traditional pattern matching method,introduces Word2 Vec and BERTopic to replace the traditional machine learning clustering algorithm,and empirically analyzes the online public opinion of"Silicon Valley Bankruptcy"on Zhihu.[Research conclusion]The results show that causal extraction integrating deep learning and sequence labeling identifies 68 613 original causality in 114 901 contexts,which is 46.47％higher than the pattern matching method;event generalization based on word vector and topic cluste-ring model classifies 2 148 representative events into 14 topics,which outperforms the generalization effect at the level of textual and se-mantic features than the K-means algorithm.The research constructs an online public opinion event evolutionary graph based on the opti-mization method,which presents the characteristics of"cyclic""tight"and"long-chain"evolutionary paths around the core topics,and the construction process and analysis process can provide tools and decision support for online public opinion management.
online public opinionevent evolutionary graphknowledge discoverydeep learningsequence labelingtopic clustering
国家科技期刊平台
NSTL
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