1School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China 2Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing 100876, China 3Beijing University of Posts and Telecommunications Library, Beijing 100876, China
[Objective] This paper studies the domain discrimination for public opinions of online communities, aiming to improve knowledge base, as well as the effectiveness of the machine learning models.[Methods] We retrieved 478,303 pieces of textual data from multiple online communities for college students. Then, we created a semantic relationship graph with a total of 5,248 nodes and 16,488 edges, which could also be extended automatically. Finally, we proposed a short text analysis model to conduct domain analysis for the texts.[Results] The F value of the proposed model reached 83.94%, which was 8.56%, 5.97% and 4.27% higher than those of the SVM, NB and CNN methods.[Limitations] The sample size needs to be expanded and the parameter feedback mechanism needs to be modified.[Conclusions] Compared with methods based on machine learning, the proposed model’s accuracy is improved. It could also conduct real-time analysis.
田钟林,吴旭,颉夏青,许晋,陆月明. 一种基于领域语义关系图的短文本实时分析模型*[J]. 数据分析与知识发现, 2020, 4(2/3): 239-248.
Tian Zhonglin,Wu Xu,Xie Xiaqing,Xu Jin,Lu Yueming. Real-time Analysis Model for Short Texts with Relationship Graph of Domain Semantics. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 239-248.
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