CNN Earthquake Public Opioion Analysis Model Based on Multi-Head Self-Attention Mechanism
XU Xiaotong
CHEN Jifeng
LI Dongping
WU LingJie
LI Huanyu
YAO Di
Abstract：With the advancement of the internet,new media platforms are progressively becoming the preferred channels for the general public to release and access earthquake disaster information,as well as one of the effective avenues for earthquake-related departments to promptly grasp the current disaster situation and public opinion.This paper employed web crawling technology to collect post-earthquake Weibo posts and comments from users and con-structed a dataset that was then subjected to preprocessing,thus laying the foundation for subsequent analysis and modeling.The paper introduced a multi-head self-attention mechanism to optimize the conventional CNN model,thereby developing a CNN earthquake public opinion analysis model based on the multi-head self-attention mecha-nism.The paper enriched the diversity of feature subspace,ensured parallel processing,captured different levels of features and information,and enhanced the model's ability to understand earthquake public opinion.The model was put into practical application and visualization by analyzing the public opinion following the Zadoi M5.5 earth-quake,in Yushu Tibetan Autonomous Prefecture,Qinghai Province on March 7th,2024.Through experimental comparisons,the constructed model achieved a weighted average F1 score of 92.9%and a macro-average F1 score of 92.1%.These results demonstrate that the model can effectively provide auxiliary support for earthquake-related departments to quickly understand the disaster situation and public opinion environment after the earthquake..
Keywords：Deep learningEarthquake public opinionMulti-head self-attention mechanismSentiment classifi-cation
Publication Date：2024-12-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 21-32 )