帮助 关于我们

返回检索结果

Topic-Feature Lattices Construction and Visualization for Dynamic Topic Number

查看参考文献18篇

文摘 The topic recognition for dynamic topic number can realize the dynamic update of super parameters,and obtain the probability distribution of dynamic topics in time dimension,which helps to clear the understanding and tracking of convection text data.However,the current topic recognition model tends to be based on a fixed number of topics K and lacks multi-granularity analysis of subject knowledge.Therefore,it is impossible to deeply perceive the dynamic change of the topic in the time series.By introducing a novel approach on the basis of Infinite Latent Dirichlet allocation model,a topic feature lattice under the dynamic topic number is constructed.In the model,documents,topics and vocabularies are jointly modeled to generate two probability distribution matrices:Documentstopics and topic-feature words.Afterwards,the association intensity is computed between the topic and its feature vocabulary to establish the topic formal context matrix.Finally,the topic feature is induced according to the formal concept analysis (FCA) theory.The topic feature lattice under dynamic topic number (TFL DTN) model is validated on the real dataset by comparing with the mainstream methods.Experiments show that this model is more in line with actual needs,and achieves better results in semi-automatic modeling of topic visualization analysis.
来源 Journal of Systems Science and Information ,2021,9(5):558-574 【核心库】
DOI 10.21078/JSSI-2021-558-17
关键词 dynamic topic number ; infinite latent Dirichlet allocation (ILDA) ; formal concept analysis ; topic feature lattice ; topic feature lattice under dynamic topic number (TFL DTN) model
地址

Bengbu Medical College, Bengbu, 233000

语种 英文
文献类型 研究性论文
ISSN 1478-9906
学科 社会科学总论
基金 Supported by the Key Projects of Social Sciences of Anhui Provincial Department of Education ;  the Natural Scientific Project of Anhui Provincial Department of Education ;  Innovation Team of Health Information Management and Application Research,BBMC
文献收藏号 CSCD:7085857

参考文献 共 18 共1页

1.  Li X. Research on the framework of news topic analysis based on fusion denoising and dynamic topic. Information Science,2018,36(4):14-21 CSCD被引 1 次    
2.  Yu M. Research on hierarchical topic detection in topic detection and tracking. Journal of Computer Research and Development,2006,43(3):489-495 CSCD被引 1 次    
3.  Lu N. Effective event evolution analysis algorithm. Application Research of Computers,2009,26(11):4101-4103 CSCD被引 1 次    
4.  Lin J. Analysis on topic evolution of news comments by combining word vector and clustering algorithm. Computer Engineering and Science,2016,38(11):2368-2374 CSCD被引 1 次    
5.  Cigarrn J. A step forward for topic detection in Twitter: An FCA-based approach. Expert Systems with Applications,2016,57:21-36 CSCD被引 4 次    
6.  Guesmi S. FCA for common interest communities discovering. 2014 International Conference on Data Science and Advanced Analytics (DSAA),2014:449-445 CSCD被引 1 次    
7.  Christidis K. Using probabilistic topic models in enterprise social software. Business Information Systems. BIS 2010. Lecture Notes in Business Information Processing, vol 47,2010:23-34 CSCD被引 1 次    
8.  Pei C. Research on microblog user clustering based on improved LDA topic model. Information Studies: Theory & Application,2016,39(3):135-139 CSCD被引 1 次    
9.  Al Sumait L. On-line LDA: Adaptive topic models for mining text streams with applications to topic detection and tracking. 2008 Eighth IEEE International Conference on Data Mining,2008:3-12 CSCD被引 1 次    
10.  Heinrich G. "Infinite LDA"-Implementing the HDP with minimum code complexity. Technical note TN2011/1,2011:1-20 CSCD被引 1 次    
11.  Blei D M. Latent dirichlet allocation. Journal of Machine Learning Research,2003,3(4/5):993-1022 CSCD被引 1372 次    
12.  Fang Y. Self-Adaptive Topic Model: A Solution to the Problem of "Rich Topics Get Richer". China Communications,2014,11(12):35-43 CSCD被引 1 次    
13.  Gershman S J. A tutorial on Bayesian nonparametric models. Journal of Mathematical Psychology,2012,56(1):1-12 CSCD被引 11 次    
14.  Feng S. Reversible measure-valued processes associated with the Poisson-Dirichlet distribution. Scientia Sinica Mathematica,2019,49(3):377-388 CSCD被引 1 次    
15.  Huillet T. Random partitioning problems involving poisson point processes on the interval. International Journal of Pure and Applied Mathematics,2005,24(2):143-179 CSCD被引 1 次    
16.  Tang X. Model construction of secondary organization of Weibo search results based on concept lattice. Information Studies: Theory & Application,2014,37(10):115-120 CSCD被引 1 次    
17.  Pang B. Extracting topics and their relationship from college student mentoring. Data Analysis and Knowledge Discovery,2018,2(6):92-101 CSCD被引 1 次    
18.  Xu W. A Chinese keyword extraction algorithm based on TFIDF method. Information Studies: Theory & Application,2008,31(2):298-302 CSCD被引 1 次    
引证文献 1 篇

1 Gan Xinye The Impact of Industrial Policy on Photovoltaic Enterprise Risk Using an LDA Based-Deep Neural Network Model Journal of Systems Science and Information,2022,10(2):181-192
CSCD被引 2 次

显示所有1篇文献

论文科学数据集
PlumX Metrics
相关文献

 作者相关
 关键词相关
 参考文献相关

版权所有 ©2008 中国科学院文献情报中心 制作维护：中国科学院文献情报中心
地址：北京中关村北四环西路33号 邮政编码：100190 联系电话：(010)82627496 E-mail:cscd@mail.las.ac.cn 京ICP备05002861号-4 | 京公网安备11010802043238号