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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (9): 65-73    DOI: 10.11925/infotech.2096-3467.2017.09.07
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Sentiment Analysis of Weibo Opinion Leaders——Case Study of “Illegal Vaccine” Event
He Yue, Zhu Can()
Business School, Sichuan University, Chengdu 610065, China
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Abstract  

[Objective] This paper tries to identify the opinion leaders of Weibo and examines their roles in information dissemination. [Methods] We adopted, a method of two-step clustering to identify opinion leaders of the “illegal vaccine” event. Then, we created a network matrix for these opinion leaders based on their relationship. Finally, we analyzed the sentiments of the Weibo users to evaluate the role of opinion leaders’ network. [Results] The overall users’ sentiments was negative. The opinion leaders’ network posed significant impacts on the sentiments of average users. [Limitations] Only examined our method with one event. [Conclusions] The celebrities and opinion leaders play important role to sway the public opinion online.

Key wordsMicro-blog      Opinion Leaders Network      Sentiment Analysis      Time Difference Correlation Analysis      Two-step Cluster     
Received: 24 April 2017      Published: 18 October 2017
ZTFLH:  G350  

Cite this article:

He Yue,Zhu Can. Sentiment Analysis of Weibo Opinion Leaders——Case Study of “Illegal Vaccine” Event. Data Analysis and Knowledge Discovery, 2017, 1(9): 65-73.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.09.07     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I9/65

特征 准则 属性描述
用户特征 真实度
覆盖度
活跃度
是否认证[5]
微博数[6]、粉丝数[13]
关注数[5]
微博影响力 影响广度
影响深度
接纳度
转发数[14]
评论数[13]
点赞数
聚类指标 聚类结果对比
第一类 第二类
用户数 7 218 924
认证比例(%) 17.20 90.80
微博数(%) 4.16 95.84
粉丝数(%) 0.90 99.10
关注数(%) 15.25 84.75
转发数(%) 0.56 99.44
评论数(%) 0.60 99.40
点赞数(%) 0.59 99.41
度数中心度
Point Centrality
中间中心度
Betweenness
Centrality
成员距离均值
Average Distance Among
Reachable Pairs
网络凝聚力指数
Distance-based
Cohesion
网络密度
Density
政府类意见领袖网 35.71 57.69 1.95 0.12 0.08
新闻媒体类意见领袖网 90.51 3.30 1.78 0.61 0.21
明星与大V类意见领袖网 82.35 7.57 2.12 0.53 0.22
企业与企业家类意见领袖网 66.67 31.95 2.92 0.37 0.09
普通网民类意见领袖网 35.29 6.72 2.62 0.42 0.07
积极情感(%) 中性情感(%) 消极情感(%)
总体 6.70 25.58 67.72
意见领袖网 7.69 55.08 37.23
普通用户 6.53 23.38 70.09
中性 积极 消极
第一个星期(%) 4.17 0.74 5.77
意见领袖网 第二个星期(%) 10.31 2.33 6.43
差值(百分点) 6.14 1.60 0.64
第一个星期(%) 18.71 4.50 66.11
普通用户 第二个星期(%) 34.14 14.59 32.20
差值(百分点) 15.43 10.09 -33.91
序列对 交叉相关系数最大值对应的滞后期(4小时) 交叉相关系数
意见领袖网的积极情感变化对普通用户的积极情感变化的影响 -6 0.252
意见领袖网的中性情感变化对普通用户的中性情感变化的影响 -3 0.217
意见领袖网的消极情感变化对普通用户的消极情感变化的影响 -3 0.195
意见领袖网的中性情感变化对普通用户的消极情感变化的影响 0 -0.295
意见领袖网的积极情感变化对普通用户的消极情感变化的影响 0 -0.186
意见领袖网的消极情感变化对普通用户的中性情感变化的影响 0 -0.357
意见领袖网类型 积极情感(%) 中性情感(%) 消极情感(%)
新闻媒体类 6.86 66.67 26.47
政府类 38.89 50.00 11.11
明星与大V类 20.97 24.19 54.84
企业与企业家类 2.94 36.76 60.30
普通网民类 3.94 16.53 79.53
普通用户消极情感
度数中心度 Pearson 相关性 -.924
显著性(双侧) .076
网络凝聚力指数 Pearson 相关性 -.912
显著性(双侧) .088
积极 中性 消极
新闻媒体类 第一个星期(%) 4.76 32.90 37.40
第二个星期(%) 5.61 39.80 13.27
差值(百分点) 0.85 6.90 -24.13
政府类 第一个星期(%) 0.39 0.13 0.13
第二个星期(%) 2.04 4.08 0.51
差值(百分点) 1.65 3.95 0.38
明星与大V类 第一个星期(%) 0.90 1.16 2.70
第二个星期(%) 3.06 3.06 6.63
差值(百分点) 2.16 1.90 3.93
企业与企业家类 第一个星期(%) 0.13 2.17 4.50
第二个星期(%) 0.51 4.09 3.06
差值(百分点) 0.38 1.92 -1.44
普通网民类 第一个星期(%) 0.39 1.93 10.41
第二个星期(%) 1.02 3.06 10.20
差值(百分点) 0.63 1.13 -0.21
序列对 交叉相关系数最大值对应的滞后期
(4小时)
交叉相关系数
新闻媒体类意见领袖网积极情感变化对普通用户积极情感变化的影响 -1 0.215
政府类意见领袖网积极情感变化对普通用户积极情感变化的影响 -2 0.164
明星与大V类意见领袖网积极情感变化对普通用户积极情感变化的影响 -6 0.415
企业与企业家类意见领袖网积极情感变化对普通用户积极情感变化的影响 -4 0.152
普通网民类意见领袖网积极情感变化对普通用户积极情感变化的影响 -1 0.147
序列对 交叉相关系数最大值对应的滞后期
(4小时)
交叉相关系数
新闻媒体类意见领袖网中性情感变化对普通用户中性情感变化的影响 -3 0.299
政府类意见领袖网中性情感变化对普通用户中性情感变化的影响 5 0.341
明星与大V类意见领袖网中性情感变化对普通用户中性情感变化的影响 -4 0.189
企业与企业家类意见领袖网中性情感变化对普通用户中性情感变化的影响 -4 0.171
普通网民类意见领袖网中性情感变化对普通用户中性情感变化的影响 0 0.162
序列对 交叉相关系数最大值对应的滞后期
(4小时)
交叉相关系数
新闻媒体类意见领袖网消极情感变化对普通用户消极情感变化的影响 -2 0.391
政府类意见领袖网消极情感变化对普通用户消极情感变化的影响 -6 0.158
明星与大V类意见领袖网消极情感变化对普通用户消极情感变化的影响 -5 0.153
企业与企业家类意见领袖网消极情感变化对普通用户消极情感变化的影响 2 0.144
普通网民类意见领袖网消极情感变化对普通用户消极情感变化的影响 0 0.269
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