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 ...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.展开更多
Many social events spread fast through the Internet and arouse wide community discussions. Those on-line public opinions emerge into diverse topics along the time. Moreover, the strength of the topics is fluctuating. ...Many social events spread fast through the Internet and arouse wide community discussions. Those on-line public opinions emerge into diverse topics along the time. Moreover, the strength of the topics is fluctuating. How to catch both primary topics and trend of topics over the shifting on-line discussions are not only of theoretical importance for scientific research, but also of practical importance for societal management especially in current China. To try the cutting-edge text analytic technologies to deal with unstructured on-line public opinions and provide support for social problem-solving in the big data era is worth an endeavour. This paper applies dynamic topic model （DTM） to explore the changing topics of new posts collected from Tianya Zatan Board of Tianya Club, the most influential Chinese BBS in China's Mainland. By analysis of the hot and cold terms trends, we catch the topics shift of main on-line concerns with illustrations of topics of school bus and environment in December of 2011. An algorithm is proposed to compute the strength fluctuation of each topic. With visualized analysis of the respective main topics in several months of 2012, some patterns of the topics fluctuation on the board are summarized.展开更多
基金the Key Projects of Social Sciences of Anhui Provincial Department of Education(SK2018A1064,SK2018A1072)the Natural Scientific Project of Anhui Provincial Department of Education(KJ2019A0371)Innovation Team of Health Information Management and Application Research(BYKC201913),BBMC。
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.
基金supported by National Basic Research Program of China under Grant No.2010CB731405National Natural Science Foundation of China under Grant No.71171187&71371107
文摘Many social events spread fast through the Internet and arouse wide community discussions. Those on-line public opinions emerge into diverse topics along the time. Moreover, the strength of the topics is fluctuating. How to catch both primary topics and trend of topics over the shifting on-line discussions are not only of theoretical importance for scientific research, but also of practical importance for societal management especially in current China. To try the cutting-edge text analytic technologies to deal with unstructured on-line public opinions and provide support for social problem-solving in the big data era is worth an endeavour. This paper applies dynamic topic model （DTM） to explore the changing topics of new posts collected from Tianya Zatan Board of Tianya Club, the most influential Chinese BBS in China's Mainland. By analysis of the hot and cold terms trends, we catch the topics shift of main on-line concerns with illustrations of topics of school bus and environment in December of 2011. An algorithm is proposed to compute the strength fluctuation of each topic. With visualized analysis of the respective main topics in several months of 2012, some patterns of the topics fluctuation on the board are summarized.
