CLOpin: A Cross-Lingual Knowledge Graph Framework for Public Opinion Analysis and Early Warning
Liang Ye1,2,Li Xiaoyuan3,Xu Hang2(),Hu Yiran2
1Artificial Intelligence and Human Languages Lab, Beijing Foreign Studies University, Beijing 100089, China 2School of Information Science and Technology, Beijing Foreign Studies University, Beijing 100089, China 3School of Asian Studies, Beijing Foreign Studies University, Beijing 100089, China
[Objective] This paper explores the relationship of information mapping among different languages, aiming to effectively monitor public opinion around the world and guide domestic audience effectively. [Methods] We proposed CLOpin, a cross-linguistic knowledge-mapping framework in the field of public opinion analysis and early warning. The platform developed several toolsets for different scenarios to process cross-linguistic data sets. CLOpin could integrate data from various sources efficiently and construct a knowledge graph to implement cross-linguistic public opinion analysis and early warning. [Results] Within the first hour following breaking news, the knowledge integrity of our model was 13.9% higher than that of the single language knowledge graph models. Our model’s knowledge integrity was 5.2% lower than that of the latter in 24 hours. [Limitations] The construction of our model was constrained by the scarcity of domain experts, which is the bottleneck for the knowledge graph of non-common language. [Conclusions] The CLOpin framework help us accurately grasp public opinion and early warning accordingly.
梁野,李小元,许航,胡伊然. CLOpin:一种面向舆情分析与预警领域的跨语言知识图谱架构*[J]. 数据分析与知识发现, 2020, 4(6): 1-14.
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, 2020, 4(6): 1-14.
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