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Data Analysis and Knowledge Discovery  0, Vol. Issue (): 1-    DOI: 10.11925/infotech. 2096-3467.2019.1145
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CLOpin: A Cross-lingual Knowledge Graph Framework for Public Opinion Analysis and Early Warning
Liang Ye,Li Xiaoyuan,Xu Hang,Hu Yiran
(School of Information Science and Technology, Beijing Foreign Studies University, Beijing 100089, China)
(School of Asian Studies, Beijing Foreign Studies University, Beijing 100089, China)
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[Objective] To explore the relation of information mapping among different languages, so as to achieve effective monitoring of public opinion outside the country and provide positive guidance to domestic audiences. [Method] A cross-linguistic knowledge mapping platform CLOpin is proposed in the field of public opinion analysis and early warning. The platform designs several toolsets for different scenarios to process cross-linguistic data sets, which can integrate data from various sources efficiently and construct a knowledge mapping to guide the implementation of cross-linguistic public opinion analysis and early warning. [Result] The results show that the information integrity of CLKG in one hour is 13.9% higher than single language knowledge graph, and only 5.2% lower than that of the latter in 24 hours. [Limitations] The construction of CLKG is constrained by the scarcity of domain experts, which has become the bottleneck of the construction of knowledge graph of non-common language. [Conclusion] In CLOpin platform, knowledge from different sources complements each other, which has a significant effect on expanding the amount of event information, and is conducive to accurately grasping the dynamics of public opinion and making early warning accordingly.

Key words Cross-lingual      knowledge graph      Public opinion analysis      Early warning      
Published: 23 April 2020
ZTFLH:  TP393,G250  

Cite this article:

Liang Ye, Li Xiaoyuan, Xu Hang, Hu Yiran. CLOpin: A Cross-lingual Knowledge Graph Framework for Public Opinion Analysis and Early Warning . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL: 2096-3467.2019.1145     OR

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