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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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[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 wordsSemantic Network      Public Opinion      Subject Discovery      Knowledge Map     
Received: 10 February 2020      Published: 10 July 2020
ZTFLH:  TP391  
Corresponding Authors: Mu Dongmei     E-mail:

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:     OR

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 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 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
[1] 黄楚新, 吴信训, 唐绪军. 中国新媒体发展报告No.9(2018) [R/OL]. [ 2018- 06- 27]. 4456184.shtml.
[1] ( Huang Chuxin, Wu Xinxun, Tan Xujun. Annual Report on the Development of New Media in China No.9(2018)[R/OL]. [ 2018- 06- 27].
[2] 刘毅. 略论网络舆情的概念、特点、表达与传播[J]. 理论界, 2007(1):11-12.
[2] ( Liu Yi. On the Concept, Characteristics, Expression and Dissemination of Online Public Opinion[J]. Theory Horizon, 2007(1):11-12.)
[3] 中华人民共和国国务院. 突发公共卫生事件应急条例[M]. 北京: 中国法制出版社, 2003.
[3] ( State Council of the People’s Republic of China. Regulations on Preparedness for and Response to Emergent Public Health Hazards[M]. Beijing: China Legal Publishing House, 2003.)
[4] D’Andrea E, Ducange P, Bechini A, et al. Monitoring the Public Opinion about the Vaccination Topic from Tweets Analysis[J]. Expert Systems with Application, 2019,116:209-226.
doi: 10.1016/j.eswa.2018.09.009
[5] Wang G H, Chi Y X, Liu Y J, et al. Studies on a Multidimensional Public Opinion Network Model and Its Topic Detection Algorithm[J]. Information Processing & Management, 2019,56(3):584-608.
doi: 10.1016/j.ipm.2018.11.010
[6] Geng X, Zhang Y M, Jiao Y H, et al. A Novel Hybrid Clustering Algorithm for Topic Detection on Chinese Microblogging[J]. IEEE Transactions on Computational Social Systems, 2019,6(2):289-300.
doi: 10.1109/TCSS.2019.2897641
[7] 廖海涵, 王曰芬, 关鹏. 微博舆情传播周期中不同传播者的主题挖掘与观点识别[J]. 图书情报工作, 2018,62(19):77-85.
[7] ( Liao Haihan, Wang Yuefen, Guan Peng. Topic Mining and Viewpoint Recognition of Different Communicators in the Transmission Cycle of Micro-blog Public Opinion[J]. Library and Information Service, 2018,62(19):77-85.)
[8] 李磊, 刘继, 张竑魁. 基于共现分析的网络舆情话题发现及态势演化研究[J]. 情报科学, 2016,34(1):44-47, 57.
[8] ( Li Lei, Liu Ji, Zhang Hongkui. Topics Identification and Evolution Trend of Network Public Opinion Based on Co-occurrence Analysis[J]. Information Science, 2016,34(1):44-47, 57.)
[9] 陈晓美, 高铖, 关心惠. 网络舆情观点提取的LDA主题模型方法[J]. 图书情报工作, 2015,59(21):21-26.
[9] ( Chen Xiaomei, Gao Cheng, Guan Xinhui. Extraction Method of Network Public Opinion Based on LDA Topic Model[J]. Library and Information Service, 2015,59(21):21-26.)
[10] 林丽娜, 魏德志. 一种基于时间序列网络舆情热点事件发现模型[J]. 太原师范学院学报(自然科学版), 2016,15(3):52-56.
[10] ( Lin Lina, Wei Dezhi. Sort Model about Hotspots Public Opinion Event Based on Time-series in the Context of Large Data[J]. Journal of Taiyuan Normal University (Natural Science Edition), 2016,15(3):52-56.)
[11] 张寿华, 刘振鹏. 网络舆情热点话题聚类方法研究[J]. 小型微型计算机系统, 2013,34(3):471-474.
[11] ( Zhang Shouhua, Liu Zhenpeng. Study on Clustering Method for Internet Public Opinion Hotspot Topic[J]. Journal of Chinese Computer Systems, 2013,34(3):471-474.)
[12] 吴江, 赵颖慧, 高嘉慧. 医疗舆情事件的微博意见领袖识别与分析研究[J]. 数据分析与知识发现, 2019,3(4):53-62.
[12] ( Wu Jiang, Zhao Yinghui, Gao Jiahui. Research on Weibo Opinion Leaders Identification and Analysis in Medical Public Opinion Incidents[J]. Data Analysis and Knowledge Discovery, 2019,3(4):53-62.)
[13] 王林, 王可, 吴江. 社交媒体中突发公共卫生事件舆情传播与演变——以2018年疫苗事件为例[J]. 数据分析与知识发现, 2019,3(4):42-52.
[13] ( Wang Lin, Wang Ke, Wu Jiang. Public Opinion Propagation and Evolution of Public Health Emergencies in Social Media Era: A Case Study of 2018 Vaccine Event[J]. Data Analysis and Knowledge Discovery, 2019,3(4):42-52.)
[14] 袁野, 兰月新, 李增, 等. 基于聚类分析的网络舆情热点事件分类研究[J]. 内江科技, 2017,38(5):80-82.
[14] ( Yuan Ye, Lan Yuexin, Li Zeng, et al. Classification Research on Hotspot Events of Network Public Opinion Based on Cluster Analysis[J]. Neijiang Science & Technology, 2017,38(5):80-82.)
[15] Liao J, Wang S G, Li D Y. Identification of Fact-implied Implicit Sentiment Based on Multi-level Semantic Fused Representation[J]. Knowledge-Based Systems, 2019,165:197-207.
doi: 10.1016/j.knosys.2018.11.023
[16] Choi H S, Lee H. Multitask Learning Approach for Understanding the Relationship Between Two Sentences[J]. Information Sciences, 2019,485:413-426.
doi: 10.1016/j.ins.2019.02.026
[17] Sayyadi H, Raschid L. A Graph Analytical Approach for Topic Detection[J]. ACM Transactions on Internet Technology, 2013, 13(2): Article No.4.
[18] Corbyn Z. Twitter to Track Dengue Fever Outbreaks in Brazil[J]. New Scientist, 2011,211(2821):18.
[19] 李纲, 陈璟浩. 突发公共事件网络舆情研究综述[J]. 图书情报知识, 2014(2):111-119.
[19] ( Li Gang, Chen Jinghao. A Review of Network Public Opinion for Unexpected Emergency[J]. Documentation, Information & Knowledge, 2014(2):111-119.)
[20] 曾建勋, 魏来. 大数据时代的情报学变革[J]. 情报学报, 2015,34(1):37-44.
[20] ( Zeng Jianxun, Wei Lai. The Changes of Information Science in Big Data Era[J]. Journal of the China Society for Scientific and Technical Information, 2015,34(1):37-44.)
[21] 李颖, 郝晓燕, 王勇. 中文开放式多元实体关系抽取[J]. 计算机科学, 2017,44(Z1):80-83.
[21] ( Li Ying, Hao Xiaoyan, Wang Yong. N-ary Chinese Open Entity-relation Extraction[J]. Computer Science, 2017,44(Z1):80-83.)
[22] 语言云[EB/OL]. [ 2019- 07- 08].
[22] ( Language Cloud[EB/OL]. [ 2019- 07- 08].
[23] 孙盼盼. 基于依存语法的语义角色标注语料库构建研究[D]. 烟台: 鲁东大学, 2018.
[23] ( Sun Panpan. A Study on Building a Semantic Role Labeling Corpus Based on Dependency Grammar[D]. Yantai: Ludong University, 2018.)
[24] 毛小丽, 何中市, 邢欣来, 等. 基于语义角色的实体关系抽取[J]. 计算机工程, 2011,37(17):143-145.
doi: 10.3969/j.issn.1000-3428.2011.17.048
[24] ( Mao Xiaoli, He Zhongshi, Xing Xinlai, et al. Entity Relation Extraction Based on Semantic Role[J]. Computer Engineering, 2011,37(17):143-145.)
doi: 10.3969/j.issn.1000-3428.2011.17.048
[25] 李明耀, 杨静. 基于依存分析的开放式中文实体关系抽取方法[J]. 计算机工程, 2016,42(6):201-207.
doi: 10.3969/j.issn.1000-3428.2016.06.036
[25] ( Li Mingyao, Yang Jing. Open Chinese Entity Relation Extraction Method Based on Dependency Parsing[J]. Computer Engineering, 2016,42(6):201-207.)
doi: 10.3969/j.issn.1000-3428.2016.06.036
[26] 秦晓慧, 侯霞, 赵雪. 一种融合语义角色和依存句法的实体关系抽取算法[J]. 北京信息科技大学学报(自然科学版), 2019,34(1):64-67, 98.
[26] ( Qin Xiaohui, Hou Xia, Zhao Xue. An Entity Relation Extraction Algorithm Based on Semantic Roles Labeling and Dependency Parsing[J]. Journal of Beijing Information Science & Technology University (Natural Science Edition), 2019,34(1):64-67, 98.)
[27] 刘焕勇. 事件三元组抽取[EB/OL].
[27] ( Liu Huanyong. Event Triples Extraction[EB/OL].
[28] 牟冬梅, 邵琦, 韩楠楠, 等. 微博舆情多维度社会属性分析与可视化研究——以某疫苗事件为例[J]. 图书情报工作, 2020,64(3):111-118.
[28] ( Mu Dongmei, Shao Qi, Han Nannan, et al. Research on Multi-dimensional Social Attribute Analysis and Visualization of Weibo Public Opinion——Taking a Vaccine Event as an Example[J]. Library and Information Service, 2020,64(3):111-118.)
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