Risk Early Warning for Police-Related Online Public Opinion Based on Deep Learning
DOI:
https://doi.org/10.4108/eetsis.10664Keywords:
Police-related online public opinion, Deep learning, Risk early warning, Optimization algorithmAbstract
INTRODUCTION: Police-related online public opinion is highly sensitive and can easily have a negative impact on social stability.
OBJECTIVES: This paper aims to address the crucial need for early warning systems for the risks associated with police-related online public opinion to ensure social harmony and effectively prevent and resolve major social risks.
METHODS: Based on a literature review and deep learning methods, this research constructs an indicator system from four dimensions, analyzing data from representative police-related online public opinion incidents over the past five years. A CNN-BiLSTM sentiment classification model is built for sentiment analysis, and an optimized SSA-CNN-LSTM-Attention model is used for public opinion risk early warning.
RESULTS: The experimental results demonstrate that the SSA-CNN-LSTM-Attention model has the minimum error.
CONCLUSION: This research provides a theoretical reference for public security organs in responding to and preventing police-related online public opinion.
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Copyright (c) 2025 Juan Wang, Yuxiang Guan, Nan Wang, Jie Pan, Peng Zhang
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