互联网舆情事件影响分析与动态演化研究

发布时间：2018-03-20 19:14

本文选题：舆情分析　切入点：影响评价　出处：《天津大学》2015年博士论文　论文类型：学位论文

【摘要】：当今网络化和大数据时代,社会媒体已成为舆情事件发生、发展与演化的重要阵地。研发基于社会媒体网上—网下实时交互的舆情态势精准感知与态势研判技术,提升突发事件应对处置的能力,既是学术研究的热点与难点,也是商业、社会乃至国家安全面临的急迫需求与重大任务。在此背景下,本论文基于社会媒体舆情事件的多源异构大数据信息,主要工作包括如下三个方面:(1)融合多源异构信息进行舆情事件影响分析。针对网络舆情数据多维度、多来源、信息异构化等内在特征,及现有舆情指标体系覆盖度不够、指标项选取难以多维量化和跨源融合等问题,提出了融合多源和异构信息的舆情事件影响量化评价体系。该体系融合了新闻网站、论坛网站及微博网站等多种数据源,设计了基于情感计算、文本挖掘和用户社会网络分析的评价指标项,并采用层次模糊评价方法进行多维指标权重计算,实现了数据与专家领域知识的有机集成。实验表明,本论文提出的量化评价体系,能够较为准确和全面地刻画网络舆情事件的影响,与事件发展过程有较好的符合度,优于基于单一来源的事件评价方法。(2)研究了面向舆情事件的实体词抽取与扩展演变挖掘。针对舆情事件发展过程中事件描述实体词动态变化的问题,提出了基于双层模型的事件描述实体词抽取方法,采用融合用户关联关系网络的查询词扩展方法对实体词进行自动扩展,实现了对事件演化过程中中文变体词的识别。并将所提方法应用于舆情事件的数据采集查询词扩展,在事件数据采集中得到了实验验证。实验表明,本论文方法提供了良好的实体词抽取效果,以及实体词扩展词和变体词识别结果,应用于舆情事件数据采集可提升数据采集的召回率,尤其是在事件描述关键词包含“屏蔽敏感词”的情况下效果明显。(3)研究了面向舆情事件的基于异质信息网络的社区和话题动态演化分析方法。针对舆情事件发展过程中用户与话题社区数量不确定、结构不连续及两者不能协同演化的问题,构建了基于异质信息网络理论的舆情事件用户社区与话题演化分析模型,实现了基于狄利克雷过程混合模型的异质社区的协同发现及演化。实验表明,论文构建的模型能准确地刻画舆情事件的动态社区演化过程,较好地解决舆情事件演化过程中社区数量自动学习、社区结构平滑演化、社区与话题协同发现等难题。
[Abstract]:In the era of network and big data, social media has become an important position for the occurrence, development and evolution of public opinion events. It is not only the hot and difficult point of academic research, but also the urgent need and important task for business, society and even national security to improve the ability of dealing with emergencies. This paper is based on the social media public opinion event multi-source heterogeneous big data information, the main work includes the following three aspects: 1) fusion of multi-source heterogeneous information for public opinion event impact analysis. Such internal characteristics as information isomerization, insufficient coverage of the existing public opinion index system, difficulty in selecting index items for multidimensional quantification and cross-source fusion, etc. This paper proposes a quantitative evaluation system of the influence of public opinion events, which integrates multi-source and heterogeneous information. The system integrates various data sources, such as news website, forum website and Weibo website, and designs a calculation based on emotion. Text mining and user social network analysis of the evaluation index items, and the use of hierarchical fuzzy evaluation method for multi-dimensional index weight calculation, to achieve the organic integration of data and expert domain knowledge. The quantitative evaluation system proposed in this paper can accurately and comprehensively depict the influence of network public opinion events and has a good consistency with the event development process. It is better than the event evaluation method based on single source to study the entity word extraction and extended evolution mining for public opinion event. Aiming at the problem of the event describing the dynamic change of entity word during the development of public opinion event, An event description entity word extraction method based on two-layer model is proposed, and the query word extension method based on user relational network is used to extend entity word automatically. This paper realizes the recognition of Chinese variant words in the process of event evolution, and applies the proposed method to extend the query words of public opinion event data collection, and gets the experimental verification in the event data collection. The experimental results show that the proposed method can be applied to the data acquisition of public opinion events. This paper provides a good result of entity word extraction, and the result of entity word extension and variant word recognition, which can improve the recall rate of public opinion event data collection. In particular, when the event description keyword contains "masking sensitive words", the effect is obvious. (3) the analysis method of community and topic dynamic evolution based on heterogeneous information network for public opinion event is studied. Aiming at the development of public opinion event, this paper studies the analysis method of community and topic dynamic evolution based on heterogeneous information network. The number of users and topic communities in the process is uncertain, Based on heterogeneous information network theory, the analysis model of user community and topic evolution of public opinion events is constructed. The collaborative discovery and evolution of heterogeneous communities based on the Delikley process hybrid model are realized. Experiments show that the model constructed in this paper can accurately depict the dynamic community evolution process of public opinion events. It can solve the problems such as automatic learning of community quantity, smooth evolution of community structure and cooperative discovery of community and topic in the process of evolution of public opinion events.
【学位授予单位】：天津大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：G206;G254

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