公共危机事件的挖掘、分析和演化方法研究
发布时间：2018-09-05 14:30
【摘要】：在网络信息爆炸的年代,网络舆情对人们日常生活的影响越来越大。网络舆情中的公共危机事件常常会成为人们关注的焦点,其中的恶性发展的公共危机事件对社会稳定和人们的生命财产安全都有着严重的危害。因此,及时识别公共危机事件,分析事件演化,对把握舆论导向,具有重要意义。本文分别研究了网络新闻中的公共危机事件识别和微博中的危机事件演化。 在公共危机事件识别方面,因为触发词是事件的核心标识,本文研究以触发词为核心的事件识别方法。首先采用扩展触发词的方式弥补单纯使用触发词方法召回率较低的缺陷,但是这样就造成了准确率的降低。因此使用基于规则的方法对扩展触发词方法进行改造。又提出了改进的Apriori算法用以挖掘规则关键项,节省了很多人工定义规则的时间。另外,本文对基于统计的分类方法进行了一系列研究,提出了分层标记的特征选择方法,并以之优化最大熵分类器来完成事件识别任务。 在危机事件演化方面,首先结合已有的情感词典NTUSD和微博常用表情的中文描述构建微博情感词典；然后研究微博中公共危机事件的生命周期,并结合已有研究指出情感分布符合泊松分布规律,并提出了计算泊松分布拟合参数的算法IIA,通过该算法可以计算出微博中的热点事件是否可以拟合泊松分布及其拟合参数；最后通过事件情感词的权重差来判断热点事件发展趋势,若情感极性呈负向则认为事件有可能发展成为公共危机事件。 除此以外,本文设计并实现了公共危机事件识别系统,该系统是对理论知识的实际应用,可以用来对网络新闻进行公共危机事件识别,及时发现公共危机事件。 本文的创新点有以下两个方面： 1.提出分层标记的特征选择方法进行特征简约和包装,提高分类性能； 2.提出增量迭代接近算法,用于估计泊松分布的拟合参数。
[Abstract]:In the era of network information explosion, network public opinion has more and more influence on people's daily life. The public crisis event in the network public opinion often becomes the focus that people pay close attention to, the public crisis event of the malignant development among them has serious harm to the social stability and people's life and property safety. Therefore, it is of great significance to identify public crisis events and analyze the evolution of public crisis events in time to grasp the guidance of public opinion. This paper studies the identification of public crisis events in network news and the evolution of crisis events in Weibo. In the aspect of public crisis event recognition, because trigger word is the core of event identification, this paper studies the method of event recognition with trigger word as the core. Firstly, the method of extended trigger word is used to compensate for the low recall rate of the simple trigger word method, but this results in the reduction of the accuracy rate. Therefore, the extended trigger word method is modified by rule-based method. An improved Apriori algorithm is proposed to mine the key items of the rules, which saves a lot of time of manually defining the rules. In addition, this paper makes a series of researches on the classification method based on statistics, proposes a method of feature selection based on hierarchical markers, and uses it to optimize the maximum entropy classifier to complete the task of event recognition. In the aspect of crisis event evolution, we first combine the existing emotion dictionary (NTUSD) with the Chinese description of Weibo's commonly used expressions, and then study the life cycle of public crisis events in Weibo. Combined with the previous studies, it is pointed out that the distribution of emotion accords with the law of Poisson distribution, and the algorithm IIA, for calculating the fitting parameters of Poisson distribution can be used to calculate whether the hot events in Weibo can fit the Poisson distribution and its fitting parameters. Finally, the development trend of hot events is judged by the weight difference of event affective words. If the emotional polarity is negative, the event may develop into a public crisis event. In addition, this paper designs and implements a public crisis event identification system, which is a practical application of theoretical knowledge and can be used to identify public crisis events in network news and to discover public crisis events in time. The innovation of this paper has the following two aspects: 1. In order to improve the classification performance, a feature selection method based on stratified marking is proposed to simplify and package the features. 2. An incremental iterative approach algorithm is proposed to estimate the fitting parameters of Poisson distribution.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP393.092;D035.2
[Abstract]:In the era of network information explosion, network public opinion has more and more influence on people's daily life. The public crisis event in the network public opinion often becomes the focus that people pay close attention to, the public crisis event of the malignant development among them has serious harm to the social stability and people's life and property safety. Therefore, it is of great significance to identify public crisis events and analyze the evolution of public crisis events in time to grasp the guidance of public opinion. This paper studies the identification of public crisis events in network news and the evolution of crisis events in Weibo. In the aspect of public crisis event recognition, because trigger word is the core of event identification, this paper studies the method of event recognition with trigger word as the core. Firstly, the method of extended trigger word is used to compensate for the low recall rate of the simple trigger word method, but this results in the reduction of the accuracy rate. Therefore, the extended trigger word method is modified by rule-based method. An improved Apriori algorithm is proposed to mine the key items of the rules, which saves a lot of time of manually defining the rules. In addition, this paper makes a series of researches on the classification method based on statistics, proposes a method of feature selection based on hierarchical markers, and uses it to optimize the maximum entropy classifier to complete the task of event recognition. In the aspect of crisis event evolution, we first combine the existing emotion dictionary (NTUSD) with the Chinese description of Weibo's commonly used expressions, and then study the life cycle of public crisis events in Weibo. Combined with the previous studies, it is pointed out that the distribution of emotion accords with the law of Poisson distribution, and the algorithm IIA, for calculating the fitting parameters of Poisson distribution can be used to calculate whether the hot events in Weibo can fit the Poisson distribution and its fitting parameters. Finally, the development trend of hot events is judged by the weight difference of event affective words. If the emotional polarity is negative, the event may develop into a public crisis event. In addition, this paper designs and implements a public crisis event identification system, which is a practical application of theoretical knowledge and can be used to identify public crisis events in network news and to discover public crisis events in time. The innovation of this paper has the following two aspects: 1. In order to improve the classification performance, a feature selection method based on stratified marking is proposed to simplify and package the features. 2. An incremental iterative approach algorithm is proposed to estimate the fitting parameters of Poisson distribution.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP393.092;D035.2
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