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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (9): 68-80    DOI: 10.11925/infotech.2096-3467.2020.0117
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Identifying Subjects of Online Opinion from Public Health Emergencies
Shao Qi1,Mu Dongmei1,2(),Wang Ping1,Jin Chunyan1
1School of Public Health, Jilin University, Changchun 130021, China
2The First Hospital of Jilin University, Changchun 130000, China
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Abstract  

[Objective] This paper proposes a framework for identifying subjects of online opinion from public health emergencies, aiming to utilizing the advantages of semantic recognition.[Methods] First, we constructed RDF triples with dependency parsing analysis and semantic role annotations from the perspectives of grammar, semantics, and pragmatics.Then, we decided the core nodes based on degrees of the semantic graph and PageRank values. Finally, we conducted an empirical study to discover the subjects of public opinion.[Results] We successfully constructed a semantic graph for public opinion topics, and discovered the core nodes focusing on events and governments.[Limitations] The depth of semantic recognition needs to be improved.[Conclusions] The proposed model could help us identify public opinion topics.
Key words： Semantic Network      Public Opinion      Subject Discovery      Knowledge Map     
Received: 10 February 2020      Published: 10 July 2020
ZTFLH:  TP391  
Corresponding Authors: Mu Dongmei     E-mail: moudm@jlu.edu.cn
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Qi Shao
Dongmei Mu
Ping Wang
Chunyan Jin

Cite this article:

Shao Qi,Mu Dongmei,Wang Ping,Jin Chunyan. Identifying Subjects of Online Opinion from Public Health Emergencies. Data Analysis and Knowledge Discovery, 2020, 4(9): 68-80.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0117     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I9/68

Co-occurrence Topic Atlas and Semantic Topic Atlas
Subject Discovery of Public Opinion from Public Health Emergencies Network
Data Conversion Level
Schematic Diagram of Dependency Parsing Analysis
标签 含义
A0 施事
A1 受事
A2 影响范围
A3 动作开始
A4 动作结束
A5 其他动词相关
Event Core Semantic Role
Subject Discovery Process
Source Label Target Weight
国家药监局 会同 吉林省局 341
吉林省纪委监委 调查 长生疫苗 210
国家药监局 责令 停止生产 194
吉林省纪委监委 采取 三项措施 174
吉林省纪委监委 抓紧 工作 172
长春长生 生产 狂犬病疫苗 161
长春长生 发布 声明 89
长春长生 被没收 库存 81
国家药监局 通报 长春长生违法 75
Subject Triples of a Vaccine Event (Partial)
Semantic Subject Map of a Vaccine Event
ID Label 入度 出度 度 PageRank
1 长春长生 15 75 90 0.002 700
2 长生生物 15 47 62 0.002 533
3 国家药监局 1 57 58 0.000 560
4 疫苗 24 9 33 0.004 699
5 事 28 2 30 0.013 243
6 问题 27 2 29 0.006 909
7 深交所 2 20 22 0.000 335
8 长春新区公安分局 0 20 20 0.000 303
9 狂犬疫苗 4 10 14 0.001 092
10 疫苗事件 1 12 13 0.000 560
Subject Discovery Results (Partial)
Semantic Subject Map of the Subject
Semantic Subject Map of Official Government
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