基于相空间的社交网络舆情智能化检测方法
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摘要
为了提高对社交网络舆情监测和管理能力,本文提出一种基于相空间重构的社交网络舆情智能化检测方法。在重构的相空间中提取社交网络舆情采样时间序列的统计平均值和信息熵;采用混沌非线性时间序列分析方法进行社交网络舆情采样序列的回归分析;对采集的社交网络舆情采用判决反馈均衡调节方法进行社交网络舆情统计特征量混频处理,提取社交网络舆情的统计信息特征量;采用支持向量机方法进行社交网络舆情的状态评估和标准性对比,在重构的相空间中实现社交网络舆情智能化检测。仿真结果表明,该方法进行社交网络舆情检测的准确性较高,智能性较好。
In order to improve the ability of monitoring and managing public opinion in social networks, an intelligent detection method for public opinion in social networks based on phase space reconstruction is proposed. The phase space is reconstructed by using the non-linear processing method of social network public opinion sampling sequence. The statistical average and information entropy of social network public opinion sampling time series are extracted from the reconstructed phase space. The chaotic non-linear time series analysis method is used to carry out the regression analysis of social network public opinion sampling sequence, and the collected social network public opinion is analyzed. Feedback balanced adjustment method is used to mix the statistical features of social network public opinion, extract the statistical information features of social network public opinion, and support vector machine method is used to evaluate the status of social network public opinion and compare the standards. Intelligent detection of social network public opinion is realized in reconstructed phase space. The simulation results show that the method is accurate and intelligent in social network public opinion detection.
In order to improve the ability of monitoring and managing public opinion in social networks, an intelligent detection method for public opinion in social networks based on phase space reconstruction is proposed. The phase space is reconstructed by using the non-linear processing method of social network public opinion sampling sequence. The statistical average and information entropy of social network public opinion sampling time series are extracted from the reconstructed phase space. The chaotic non-linear time series analysis method is used to carry out the regression analysis of social network public opinion sampling sequence, and the collected social network public opinion is analyzed. Feedback balanced adjustment method is used to mix the statistical features of social network public opinion, extract the statistical information features of social network public opinion, and support vector machine method is used to evaluate the status of social network public opinion and compare the standards. Intelligent detection of social network public opinion is realized in reconstructed phase space. The simulation results show that the method is accurate and intelligent in social network public opinion detection.
引文
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[19]杨奕卓,于洪涛,黄瑞阳,等.基于融合表示学习的跨社交网络用户身份匹配[J].计算机工程,2018,44(9):45-51.
[2]房正宏,王冲.论网络舆情的决策价值及危机治理阜阳师范学院学报(社会科学版),2017(3):117-122.
[3]郎利影,王勇,白文庆,等.基于压缩感知CoSaMP算法的精确重构[J].计算机应用研究,2015,32(8):2554-2557.
[4]王刚,郭雪梅.社交网络环境下基于用户行为分析的个性化推荐服务研究[J].情报理论与实践,2018,41(8):102-107.
[5]柳益君,何胜,吴智勤,等.基于用户社交网络分析的高校图书馆主题多样性阅读推荐[J].图书情报工作,2018,62(8):67-73.
[6]彭舰,王屯屯,陈瑜,等.基于跨平台的在线社交网络用户推荐研究[J].通信学报,2018,39(3):147-158.
[7]李国良,楚娅萍,冯建华,等.多社交网络的影响力最大化分析[J].计算机学报,2016,39(4):643-656.
[8]熊丽荣,王玲燕,黄玉柱.一种联合LTR和社交网络的Top-k推荐方法[J].小型微型计算机系统,2018,39(12):2577-2584.
[9]王佳蕾,郭耀,刘志宏.基于社交网络信任关系的服务推荐方法[J].计算机科学,2018,45(S2):402-408.
[10]夏一雪,袁野,张文才,等.面向大数据的网络舆情异常数据监测与应用研究[J].现代情报,2018,38(6):80-85.
[11]连芷萱,兰月新,夏一雪,等.面向大数据的网络舆情多维动态分类与预测模型研究[J].情报杂志,2018,37(5):123-133,140.
[12]蒋知义,马王荣,邹凯,等.基于情感倾向性分析的网络舆情情感演化特征研究[J].现代情报,2018,38(4):50-57.
[13]刘雪晴.面向科研社交网络的小同行双向推荐算法[J].计算机应用与软件,2018,35(10):171-180.
[14]肖云鹏,孙华超,戴天骥,等.一种基于云模型的社交网络推荐系统评分预测方法[J].电子学报,2018,46(7):1762-1767.
[15]杨丰瑞,郑云俊,张昌.结合概率矩阵分解的混合型推荐算法[J].计算机应用,2018,38(3):644-649.
[16]马晓宁,王婷,王惠.基于相关向量机的网络舆情情感趋势预测[J].武汉大学学报(理学版),2018,64(3):237-243.
[17]王佳宁.网络事件热度分析系统研究[J].电子测试,2017(22):84-86.
[18]杨雪林.基于大数据的网络舆情监管预测算法研究[J].现代电子技术,2017,40(24):28-30.
[19]杨奕卓,于洪涛,黄瑞阳,等.基于融合表示学习的跨社交网络用户身份匹配[J].计算机工程,2018,44(9):45-51.