[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.
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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.
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