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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (11): 120-128    DOI: 10.11925/infotech.2096-3467.2018.1255
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Identifying Weibo Opinion Leaders with Text Sentiment Analysis
Fen Chen1,2(),Xiaohuan Gao1,Yue Peng1,Yuan He1,Chunxiang Xue1,2
1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
2 Jiangsu Collaborative Innovation Center of Social Safety Science and Technology, Nanjing 210094, China, Nanjing 210094, China
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Abstract  

[Objective] This study combines the external features and contents of the Weibo posts, aiming to identify online opinion leaders with the help of text sentiment analysis. [Methods] First, we identified the potential opinion leaders and introduced the Word2Vec algorithm to find new sentiment words. Then, we conducted sentiment analysis to categorize the texts as positive, negative or neutral ones. Finally, we detected and removed bloggers attracted too many negative comments. [Results] The proposed model optimized the ranking of opinion leaders, which was better than the improved PageRank algorithm, and more consistent with the Weibo data. [Limitations] We only examined our model with one piece of breaking news. [Conclusions] This paper identifies three types of online opinion leaders from the public reaction in emergency.

Key wordsText Sentiment Analysis      Opinion Leader      Weibo     
Received: 12 November 2018      Published: 18 December 2019
ZTFLH:  G206  
Corresponding Authors: Fen Chen     E-mail: Lanyan_js@126.com

Cite this article:

Fen Chen,Xiaohuan Gao,Yue Peng,Yuan He,Chunxiang Xue. Identifying Weibo Opinion Leaders with Text Sentiment Analysis. Data Analysis and Knowledge Discovery, 2019, 3(11): 120-128.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.1255     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I11/120

方法 主要思想
传统方法[4,5,6] 主观判断法, 自我报告法, 关键人物访谈法等
社会网络分析法[7,8,9,10,11,12] 根据各节点在网络中的交互关系构建网络结构, 据此识别意见领袖
PageRank法[13,14,15,16,17] 借鉴PageRank思想, 将微博用户之间的交互关系类比为传统网页之间的链接, 构建与微博用户转发、评论相对应的有向关系图
指标分析法[18,19,20,21,22,23,24] 分析意见领袖的典型特征, 建立识别意见领袖的指标体系, 通常包括一级指标类和二级指标项
影响力(0.6651) 活跃度(0.1038) 专业性(0.2311)
粉丝数 0.1208 原创微博数 0.0707 是否认证 0.1245
被@数 0.0915 关注人数 0.0122 行业性 0.0687
被转发数 0.2519 回复他人数 0.0209 媒介接触度 0.0379
被评论数 0.2009
关系 含义 实例
advmod副词性修饰语 用于改变副词的强度 “战争原本相当残酷, 将战争美化到如同娱乐活动一般, 让人反感”: advmod(残酷-4, 相当-3), 表示“相当”作为副词修饰了“残酷”这个形容词
amod形容词修饰语 修饰名词词组 “近来风靡荧屏的抗日题材电视剧, 越来越类型化”: amod(电视剧-7, 抗日-5), 表示名词性形容词“抗日”修饰了“电视剧”
nsubj名词性主语 修饰名词性主语 “不一样的抗日神剧, 好看!”: nsubj(好看-8, 剧-6), 表示“好看”修饰了名词性主语“剧”
neg否定修饰词 含义反转 “有人说剧情俗套抗日神剧神马的, 我倒觉得不错, 因为不该死的一个没死, 看着不郁闷”: neg(郁闷-28, 不-27), 表示“不”对“郁闷”进行了否定
程度级别 词语(示例) 权重 数量
“极其|extreme/最|most” 百分之百、绝对、极其 3 69
“很|very” 颇为、格外、实在 2 42
“较|more” 多、越是、较为 1 37
“稍|-ish” 稍、略为、一点 1/2 29
“欠|insufficiently” 不甚、微、没怎么 -1/2 12
“超|over” 过头、过分、偏 -1 30
标点符号 权重 标点符号 权重
“!!” 2 “。。。”、“···” 1/2
“, ”、“。” 1 “?”、“?!” -1/2
微博昵称 粉丝数 被@数 被转发数 被评论数 原创微博数 回复他人数 …… 媒体接触度 领袖值
头条新闻 1.000000000 0.231958763 1.000000000 1.000000000 1.000000000 0.000000000 …… 1.000000000 0.829111545
央视新闻 0.530858376 1.000000000 0.764969581 0.624107143 0.343150772 0.003816794 …… 0.739583333 0.651347868
八卦_我实在是太CJ了 0.100473289 0.046391753 0.691642651 0.638149351 0.04363392 0.026717557 …… 0.308035714 0.461397104
江苏身边事 0.006707475 0.054982818 0.74871918 0.260227273 0.221865622 0.003816794 …… 0.410714286 0.412328209
人民日报 0.590243392 0.577319588 0.240954211 0.111850649 0.307349176 0.003816794 …… 0.342261905 0.367892438
博主昵称 “伪领袖”
可能性
博主昵称 “伪领袖”
可能性
1 头条新闻 7.9% 9 财经网 8.4%
2 央视新闻 10% 10 评论员李铁 17.4%
3 八卦_我实在是太CJ了 9.6% 11 南方都市报 12.2%
4 江苏身边事 17.6% 12 泉在流淌 11%
5 人民日报 10.8% 13 孟祥远 12%
6 赖清辉 12.4% 14 暗访小王子 13.6%
7 南京发布 9.6% 15 创业家杂志 17.5%
8 宋坚 -
排名 本方法意见领袖 基于改进的PageRank意见领袖
1 头条新闻 新浪江苏
2 央视新闻 央视新闻
3 八卦_我实在是太CJ了 南京鼓楼医院
4 江苏身边事 江苏身边事
5 人民日报 人民日报
6 赖清辉 南京日报
7 南京发布 南方都市报
8 宋坚 财经网
9 财经网 环球时报
10 评论员李铁 南京发布
11 南方都市报 头条新闻
12 泉在流淌 新浪新闻视频
13 孟祥远 马伯庸
14 暗访小王子 法制日报
15 创业家杂志 评论员李铁
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[3] Yue He,Can Zhu. Sentiment Analysis of Weibo Opinion Leaders——Case Study of “Illegal Vaccine” Event[J]. 数据分析与知识发现, 2017, 1(9): 65-73.
[4] Xiong Tao, He Yue. The Identification and Analysis of Micro-blogging Opinion Leaders in the Network of Retweet Relationship[J]. 现代图书情报技术, 2013, (6): 55-62.
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