一种结合模型集成的舆情管理模型的研究
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摘要
针对社交网络特定网络舆情信息难以收集分析的问题,提出一种结合模型集成的网络舆情管理模型。对各大社交门户网站的采集器提出模块化的概念,在获取信息的速度和获取舆情信息的质量上相较于传统模型有较大提升。为了数据分析更加精确,提出一种新的集成学习算法。在此算法的基础之上设计一种新的分析模型,实现了对网络舆情的快速采集、高效分析。实验结果表明,模型在舆情信息采集以及舆情分析方面有较强的性能,有助于网络舆情的管理。
In order to solve the problem that it is difficult to collect and analyze the specific network public opinion information of social network, this paper proposed a public opinion management model based on model integration. We put forward the concept of modularization for the collectors of major social portal websites. Compared with the traditional model, the model improved the speed and quality of public opinion information acquisition. In order to analyze the data more accurately, we proposed a new integrated learning algorithm. On the basis of this algorithm, we designed a new analysis model to realiz the rapid collection and efficient analysis of network public opinion. The experimental results show that the model has strong performance in public opinion information collection and analysis, and is helpful to the management of network public opinion.
In order to solve the problem that it is difficult to collect and analyze the specific network public opinion information of social network, this paper proposed a public opinion management model based on model integration. We put forward the concept of modularization for the collectors of major social portal websites. Compared with the traditional model, the model improved the speed and quality of public opinion information acquisition. In order to analyze the data more accurately, we proposed a new integrated learning algorithm. On the basis of this algorithm, we designed a new analysis model to realiz the rapid collection and efficient analysis of network public opinion. The experimental results show that the model has strong performance in public opinion information collection and analysis, and is helpful to the management of network public opinion.
引文
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[4]吕鹏辉.基于网络爬虫的新浪微博数据获取方式研究[J].电脑知识与技术,2017,13(33):9-12.
[5]周中华,张惠然,谢江.基于Python的新浪微博数据爬虫[J].计算机应用,2014,34(11):3131-3134.
[6]高峰,刘震,高辉.结合有监督广度优先搜索策略的通用垂直爬虫[J].计算机工程,2018,44(11):289-299.
[7]伍育红.聚类算法综述[J].计算机科学,2015,42(S1):491-499,524.
[8]李彤,宋之杰.基于模型集成的突发事件舆情分析与趋势预测研究[J].系统工程理论与实践,2015,35(10):2582-2587.
[9]周志华.机器学习[M].北京:清华大学出版社,2016:173-181.
[10] Breiman L. Bagging predictors[J]. MachineLearning,1996,24(2):123-140
[11]刘志凯,张太红. Django框架在web开发中的应用[J].农业网络信息,2015(2):51-52.