基于灰色马尔可夫模型的网络舆情预测研究
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摘要
【目的/意义】网络舆情的发展是复杂非线性的变化过程,建立有效的数理模型对网络舆情发展趋势做出准确的预测具有重要的意义。【方法/过程】首先选用网络舆情事件的百度指数构建发展趋势的时间序列指标,经过几何平均弱化缓冲算子处理后建立改进的灰色Verhulst模型预测,最后采用马尔可夫对改进的灰色Verhulst模型预测结果进行修正。【结果/结论】选取"罗一笑事件"进行案例分析,验证了本文提出的改进的灰色马尔可夫模型在网络舆情预测中的有效性。
【Purpose/significance】The development of network public opinion is a complex and nonlinear variation process,and the establishment of effective mathematical model has important significance to make accurate prediction of development trend in network public opinion.【Method/process】Firstly, this paper uses the Baidu Index of network public event as the indicators of time series for the event development trend. Secondly, the original data sequences are processed by Geometric Average Weakening Buffer Operator(GAWBO), and an improved gray Verhulst model prediction is established. Finally, this paper uses Markov model to correct the prediction results of the improved gray Verhulst model.【Result/conclusion】The results show that the improved gray Markov model proposed in this paper is effective in the prediction of network public opinion with the case of"Luo Yixiao event".
【Purpose/significance】The development of network public opinion is a complex and nonlinear variation process,and the establishment of effective mathematical model has important significance to make accurate prediction of development trend in network public opinion.【Method/process】Firstly, this paper uses the Baidu Index of network public event as the indicators of time series for the event development trend. Secondly, the original data sequences are processed by Geometric Average Weakening Buffer Operator(GAWBO), and an improved gray Verhulst model prediction is established. Finally, this paper uses Markov model to correct the prediction results of the improved gray Verhulst model.【Result/conclusion】The results show that the improved gray Markov model proposed in this paper is effective in the prediction of network public opinion with the case of"Luo Yixiao event".
引文
[1]中国互联网络信息中心.中国互联网络发展状况统计报告[R].北京,2017.
2 崔薇,曾润喜,王国华.中国网络舆情研究文献计量分析[J].情报科学,2011,29(1):131-135.
3 游丹丹,陈福集.我国网络舆情预测研究综述[J].情报科学,2016,34(12):156-160.
4 李文杰,化存才,何伟全,等.网络舆情事件的灰色预测模型及案例分析[J].情报科学,2013,31(12):51-56.
5 游丹丹,陈福集.基于改进粒子群和BP神经网络的网络舆情预测研究[J].情报杂志,2016,35(8):156-161.
6 刘勘,李晶,刘萍.基于马尔可夫链的舆情热度趋势分析[J].计算机工程与应用,2011,47(36):170-173.
7 Bates J M,Granger C W J.The Combination of Forecasts[J].Journal of the Operational Research Society,1969,20(4):451-468.
8 蒋玉婷.数据挖掘技术在网络舆情预测中的应用[J].科技通报,2013,29(10):73-75.
9 徐敏捷,兰月新,刘冰月.基于组合预测的网络舆情数据预测模型研究[J].情报科学,2016,34(12):40-45.
10 肖新平,毛树华.灰预测与决策方法[M].北京:科学出版社,2013:1-6.
11 于剑.互联网时代网络舆情事件的演变分析及控制研究[D].南昌:南昌大学,2016.
12 叶璟,李炳军,刘芳.弱化缓冲算子对GM(1,1)模型的预测效应及适用性[J].系统工程理论与实践,2014,34(9):2364-2371.
13 魏勇,孔新海.几类强弱缓冲算子的构造方法及其内在联系[J].控制与决策,2010,25(2):196-202.
14 姜翔程,陈森发.加权马尔可夫SCGM(1,1)c模型在农作物干旱受灾面积预测中的应用[J].系统工程理论与实践,2009,29(9):179-185.
15 黄银华,彭建春,李常春,等.马尔科夫理论在中长期负荷预测中的应用[J].电力系统及其自动化学报,2011,23(5):131-136.
2 崔薇,曾润喜,王国华.中国网络舆情研究文献计量分析[J].情报科学,2011,29(1):131-135.
3 游丹丹,陈福集.我国网络舆情预测研究综述[J].情报科学,2016,34(12):156-160.
4 李文杰,化存才,何伟全,等.网络舆情事件的灰色预测模型及案例分析[J].情报科学,2013,31(12):51-56.
5 游丹丹,陈福集.基于改进粒子群和BP神经网络的网络舆情预测研究[J].情报杂志,2016,35(8):156-161.
6 刘勘,李晶,刘萍.基于马尔可夫链的舆情热度趋势分析[J].计算机工程与应用,2011,47(36):170-173.
7 Bates J M,Granger C W J.The Combination of Forecasts[J].Journal of the Operational Research Society,1969,20(4):451-468.
8 蒋玉婷.数据挖掘技术在网络舆情预测中的应用[J].科技通报,2013,29(10):73-75.
9 徐敏捷,兰月新,刘冰月.基于组合预测的网络舆情数据预测模型研究[J].情报科学,2016,34(12):40-45.
10 肖新平,毛树华.灰预测与决策方法[M].北京:科学出版社,2013:1-6.
11 于剑.互联网时代网络舆情事件的演变分析及控制研究[D].南昌:南昌大学,2016.
12 叶璟,李炳军,刘芳.弱化缓冲算子对GM(1,1)模型的预测效应及适用性[J].系统工程理论与实践,2014,34(9):2364-2371.
13 魏勇,孔新海.几类强弱缓冲算子的构造方法及其内在联系[J].控制与决策,2010,25(2):196-202.
14 姜翔程,陈森发.加权马尔可夫SCGM(1,1)c模型在农作物干旱受灾面积预测中的应用[J].系统工程理论与实践,2009,29(9):179-185.
15 黄银华,彭建春,李常春,等.马尔科夫理论在中长期负荷预测中的应用[J].电力系统及其自动化学报,2011,23(5):131-136.