[Objective] This paper aims to investigate the evolution of online public opinion by analyzing the spatial-temporal distribution patterns of topic evolution. [Methods] First, we used the LDA model to extract topics from news and then calculated the quantitative topic intensity index to measure their popularity. Second, we adopted spatial autocorrelation method to examine the distribution of topic intensity on “tourism” as well as its changes over time based on Moran’s I Index. [Results] The global distribution of topic intensity was clustered and characterized by the global Moran’s I index. The local distribution of topic intensity had hot spots, abnormal high values and low values. [Limitations] Only collected data from Xinhuanet, which might yield in-complete results. [Conclusions] The proposed method could effectively extract the spatial-temporal patterns of online public opinion, which improves the decision-making and early warning mechanism.
王璟琦,李锐,吴华意. 基于空间自相关的网络舆情话题演化时空规律分析*[J]. 数据分析与知识发现, 2018, 2(2): 64-73.
Jingqi Wang,Rui Li,Huayi Wu. The Evolution of Online Public Opinion Based on Spatial Autocorrelation. Data Analysis and Knowledge Discovery, DOI：10.11925/infotech.2096-3467.2017.0929.
(Wang Shaopeng, Peng Yan, Wang Jie, et al.Research of the Text Clustering Based on LDA Using in Network Public Opinion Analysis[J]. Journal of Shandong University: Natural Science, 2014, 49(9): 129-134.)
(Wang Yandong, Li Hao, Wang Teng, et al.The Mining and Analysis of Emergency Information in Sudden Events Based on Social Media[J]. Geomatics and Information Science of Wuhan University, 2016, 41(3): 290-297.)
(Chen Tao, Lin Jie.Comparative Analysis of Temporal-Spatial Evolution of Online Public Opinion Based on Search Engine Attention — Cases of Google Trends and Baidu Index[J]. Journal of Intelligence, 2013, 32(3): 7-10, 16.)
(Liu Guowei, Cheng Guohui, Jiang Jingui, et al.On the Evolution of the Unconventional Emergency Network Public Opinion from the Perspective of Spatial-temporal Differentiation — Taking “Shanghai 12.31 Stampede” as an Example[J]. Journal of Intelligence, 2015, 34(6): 126-130, 150.)
Heinrich G.Parameter Estimation for Text Analysis [R]. vsonix GmbH and University of Leipzig, 2008.
崔凯. 基于LDA的主题演化研究与实现[D]. 长沙: 国防科学技术大学, 2010.
(Cui Kai.The Research and Implementation of Topic Evolution Based on LDA [D]. Changsha: National University of Defense Technology, 2010.)