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基于社区发现和关键词共现的网络舆情潜在主题发现研究——以新浪微博魏则西事件为例 被引量:21

Research on Potential Subject Discovery of Network Public Opinion Based on Community Discovery and Keyword Co-occurrence—— Sina Micro-blog Wei Zexi Incident as an Example
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摘要 【目的/意义】网络舆情潜在主题指的是那些具备一旦发表就能吸引媒体和网民关注,进而引发热议或成为热点这种潜在影响力的网络舆情主题。为发现网络舆情潜在主题,本文提出了一种基于社会网络视角的网络舆情潜在主题发现方法。【方法/过程】该方法包括基于用户行为关系网络的关键微博挖掘和基于关键词共现网络的潜在主题抽取两部分。【结果/结论】实验结果证明,该方法不仅能有效挖掘网络舆情中的潜在主题,且识别出的部分潜在主题会随时间推移逐渐演变为热点主题,起到了一定的预警作用。同时,基于实验结果,总结了医疗领域网络舆情主题演化模式,为政府、企业应对该领域的网络舆情事件提供了有价值的参考。 [Purpose/significance] The potential theme of the network public opinion refers to those public opinion themes which not only will be able to attract media as well as Internet users' attention once published, but then lead to hot or be- come a hot potential influence of this network. In order to discover the potential theme of network public, opinion, this paper proposes a method of discovering the potential subject of network public opinion based on social network. [Method/pro- cess] The method includes two parts: the key micro blogging mining based on the user behavior relation network and the po- tential subject extraction based on the keyword co-occurrence network. [Result/conclusion] Experimental results show that this method can not only effectively detect potential topics in online public opinion, but also identify some potential top- ics that will evolve into hot topics over time, which plays a certain role in early wanting. At the same time, the research also summarizes the theme evolution model of the online public opinion in the medical field based on the experimental results, which provides a valuable reference for the government and enterprises to deal with online public opinion events in this field.
作者 丁晟春 王鹏鹏 龚思兰 DING Sheng-chun;WANG Peng-peng;GONG Si-lan(Nanjing University of Science and Technology, Nanj ing 210094, China)
机构地区 南京理工大学
出处 《情报科学》 CSSCI 北大核心 2018年第7期78-84,共7页 Information Science
基金 国家社会科学基金项目(15BTQ063) 国家社会科学基金重大项目(16ZDA224)
关键词 社区发现 共现分析 网络舆情 潜在主题 主题发现 演化模式 community detection co-occurrence analysis network public opinion potetial topic topic detection evolutionmodel
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