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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (8): 18-30    DOI: 10.11925/infotech.2096-3467.2017.08.03
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Identifying Topics of Online Public Opinion
Li Zhen, Ding Shengchun(), Wang Nan
Department of Information Management, Nanjing University of Science and Technology, Nanjing 210094, China
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Abstract  

[Objective] This paper aims to identify the topics of online public opinion. [Methods] We constructed a model to extract public opinion based on the information content of the Weibo posts, the relationship among the users, and user behaviors. [Results] We built a public opinion network, extracted and clustered relevant topics, constructed a two-mode network of “user-topic” and evolution of the opinion topics. The proposed method could identify topics of online public opinion effectively. [Limitations] The influence of users’ attributes on topic identification needed to be investigated. [Conclusions] We could identify the topics of online public opinion based on the social network analysis with the help of LDA model.

Key wordsNetwork Public Opinion      Social Network      LDA Model      Topic Identification      Opinion Topic     
Received: 31 May 2017      Published: 28 September 2017
ZTFLH:  TP391 G350  

Cite this article:

Li Zhen,Ding Shengchun,Wang Nan. Identifying Topics of Online Public Opinion. Data Analysis and Knowledge Discovery, 2017, 1(8): 18-30.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.08.03     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I8/18

观点主题
1. 猪肉国内贵, 国外便宜
2. 进口猪肉就像日本买电饭煲, 香港买奶粉, 不是卖国贼
3. (卖国贼说法)哗众取宠, 不代表政府和社会主流价值观, 不值得关注
4. 政府应该对农业进行补贴, 控制市场
5. 国内物价都比国外高, 愿意去国外生活
6. 双汇采用真猪肉
7. 国内物价高, 东西造的质量差
8. 双汇收购是因为美帝生猪有价格优势
9. 买便宜东西是人性使然, 是爱国行为
10. 国外农产品远渡重洋, 经海关收税后还比国内便宜, 值得深思
微博编号 所属观点
主题编号
微博编号 所属观点
主题编号
1 Topic2 115 Topic2
2 Topic1 116 Topic7
3 Topic2 117 Topic6
4 Topic9 118 Topic8
5 Topic6 119 Topic5
6 Topic10 120 Topic4
日期 4月
2日
4月
7日
4月
8日
4月
9日
4月9日
之后
平均
准确率
准确率 0.66 0.53 0.71 0.47 0.6 0.56
观点主题编号 节点入度归一化 观点主题
6 0.19 Topic5
7 0.15 Topic1
8 0.13 Topic9
9 0.13 Topic8
10 0.11 Topic6
11 0.10 Topic2
12 0.06 Topic4
13 0.06 Topic0
14 0.04 Topic3
15 0.03 Topic7
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