[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.
Wang Jingqi,Li Rui,Wu Huayi. The Evolution of Online Public Opinion Based on Spatial Autocorrelation. Data Analysis and Knowledge Discovery, 2018, 2(2): 64-73.
(Zhu Hengmin, Su Xinning, Zhang Xiangbin, et al.The Evolution Analysis of Online Public Opinion Based on Dynamic Network Model[J]. Information Studies: Theory & Application, 2010, 33(10): 75-78.)
(Hong Yu, Zhang Yu, Liu Ting, et al.Topic Detection and Tracking Review[J]. Journal of Chinese Information Processing, 2007, 21(6): 71-87.)
doi: 10.3969/j.issn.1003-0077.2007.06.011
(Lin Ping, Huang Weidong.Event Topic Evolution of Network Public Opinions: An Analysis Based on LDA Model[J]. Journal of Intelligence, 2013, 32(12): 26-30.)
[4]
王来华, 张丽红. 略论舆情空间[J]. 理论与现代化, 2008(3): 91-94.
[4]
(Wang Laihua, Zhang Lihong.Public Opinion Space[J]. Theory and Modernization, 2008 (3): 91-94.)
[5]
Sanderson M, Kohler J.Analyzing Geographic Queries[C]// Proceedings of SIGIR Workshop on Geographic Information Retrieval.2004.
[6]
万源. 基于语义统计分析的网络舆情挖掘技术研究[D]. 武汉: 武汉理工大学, 2012.
[6]
(Wan Yuan.Research on Mining of Internet Public Opinion Based on Semantic and Statistic Analysis [D]. Wuhan: Wuhan University of Technology, 2012.)
[7]
蒋锴. 含地理位置信息的社交媒体挖掘及应用[D]. 合肥: 中国科学技术大学, 2014.
[7]
(Jiang Kai.Geo-referenced Social Media Mining and Its Application [D]. Hefei: University of Science and Technology of China, 2014.)
(Wang Jinfeng, Ge Yong, Li Lianfa, et al.Spatiotemporal Data Analysis in Geography[J]. Acta Geographica Sinica, 2014, 69(9): 1326-1345.)
doi: 10.11821/dlxb201409007
[9]
Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
(Shan Bin, Li Fang.A Survey of Topic Evolution Based on LDA[J]. Journal of Chinese Information Processing, 2010, 24(6): 43-49, 68.)
doi: 10.3969/j.issn.1003-0077.2010.06.007
(Zhao Aihua, Liu Peiyu, Zheng Yan.Subtopic Division in News Topic Based on Latent Dirichlet Allocation[J]. Journal of Chinese Computer Systems, 2013, 34(4): 732-737.)
(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.)
(Shi Jianhong, Chen Xingshu, Wang Wenxian.Discovering Topic from Chinese Microblog Based on Hidden Topics Analysis[J]. Application Research of Computers, 2014, 31(3): 700-704.)
(Cao Li’na, Tang Xijin.Analysis of Topics Distribution in Geography Based on BBS[J]. Journal of Systems Science and Mathematical Sciences, 2016, 36(5): 671-682.)
(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.)
doi: 10.13203/j.whugis20140804
(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.)
doi: 10.3969/j.issn.1002-1965.2015.06.023
[18]
Heinrich G.Parameter Estimation for Text Analysis [R]. vsonix GmbH and University of Leipzig, 2008.
[19]
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.
[19]
(Cui Kai.The Research and Implementation of Topic Evolution Based on LDA [D]. Changsha: National University of Defense Technology, 2010.)
