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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (2): 64-73    DOI: 10.11925/infotech.2096-3467.2017.0929
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The Evolution of Online Public Opinion Based on Spatial Autocorrelation
Wang Jingqi1, Li Rui1,2(), Wu Huayi1,2
1(State Key Laboratory of Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)
2(Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China)
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Abstract  

[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.

Key wordsSpatial Autocorrelation      Moran’s I Index      LDA Model      Topic Evolution      Internet Public Opinion     
Received: 18 September 2017      Published: 07 March 2018
ZTFLH:  G350 K901  

Cite this article:

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0929     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I2/64

话题编号 话题名称 话题特征词
5 原油市场 原油 建议 操作 行情 市场 美国 止损 目标 油价 数据
6 食品安全 食品 产品 消费者 生产 销售 安全 市场 召回 餐饮 质量
9 党风党纪 问题 干部 违规 党员 监督 书记 单位 纪委 中央 纪律
11 楼市 房地产 城市 市场 政策 楼市 调控 房价 价格 住房 限购
12 扶贫 工作 扶贫 建设 发展 群众 开展 改革 落实 脱贫 社会
15 犯罪 犯罪 法院 案件 机关 法律 公安 诈骗 执行 电信 工作
16 道路交通 交通 记者 小区 道路 车辆 施工 进行 工程 建筑 市民
17 旅游 游客 旅游 景区 机场 交通 公园 公交 出行 线路 旅客
20 金融市场 公司 市场 企业 金融 投资 中国 银行 增长 行业 资金
22 教育 学生 教育 学校 高校 大学生 大学 教师 培训 学院 毕业生
时间段 日期 节假日时间段
t1 9.12-9.14 中秋节前
t2 9.15-9.18 中秋节
t3 9.19-9-22 中秋节后
t4 9.23-9.30 国庆节前
t5 10.1-10.8 国庆节
t6 10.9-10.16 国庆节后
时间段 t1 t2 t3 t4 t5 t6
全局Moran’s I 0.0826 0.1164 0.1544 0.1119 0.1275 0.0769
Z-score 6.63 9.31 12.34 8.94 10.16 6.18
P-Value 0 0 0 0 0 0
聚集类型 t1 t2 t3 t4 t5 t6
HH 66 89 113 101 113 71
HL 28 30 24 33 41 30
LH 3 3 8 9 8 15
LL 0 0 0 0 0 0
Not Significant 2 382 2 357 2 334 2 336 2 317 2 363
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