| 摘要: |
| 针对网络舆情的指标冗余和复杂度高不利于监管，提出了因子分析和SVM建立综合评价模型；首先利用因子分析将网络舆情的14个指标进行降维为3个公因子，其次在简化的指标体系中用遗传算法的5-折交叉优化SVM参数，建立遗传算法优化SVM的网络舆情危机预警模型，最后将两类的SVM改进为一对多算法对4种情况进行分类，得出网络舆情的预警；对2019年的10个网络舆情事件进行实证分析表明，低0.51%的误差预警充分说明了其可行性，达到了强化网络舆情的监管，而因子分析降低了指标体系的复杂性，遗传算法的5-折交叉提高了SVM分类器的学习能力，能更准确地预测训练集，并用一对多算法使得分类速度较快，对网络舆情的监管提供了帮助。 |
| 关键词: 网络舆情 因子分析 遗传算法 SVM 一对多算法 |
| DOI： |
| 分类号: |
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| Research on the Early Warning for Online Public Opinion Crisis Based on Factor Analysis and SVM |
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ZHU Guang-ting, PAN Xiao-lin
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School of Mathematical Science, Chongqing Normal University, Chongqing 401331, China
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| Abstract: |
| According to index redundancy and high complexity of online public opinions which are not conducive to supervision, a comprehensive evaluation model based on factor analysis and SVM is proposed, in this method, 14 indicators for online public opinions are reduced into three common factors by factor analysis, then 5-fold cross of genetic algorithm is used to optimize SVM parameters in the simplified index system, the early warning model for online public opinion crisis by using genetic algorithm to optimize SVM is set up, finally, two kinds of SVM are improved into one-to-many algorithm to classify four cases, as a result, the early warning on online public opinions is obtained. The empirical analysis of 10 online public opinion events in 2019 shows that the early warning error is lower than 0.51 percent, which reveal that the model is feasible and which strengthen the supervision on online public opinions. Factor analysis reduces the complexity of index system, 5-fold cross of the genetic algorithm improves the learning ability of SVM classifier, thus, the model can more accurately predict training set, one-to-many algorithm makes classification speed more quickly, which provide the help for the supervision on online public opinions. |
| Key words: online public opinion factor analysis genetic algorithm SVM one-to-many |
