CASIA OpenIR
An Interactive Visualization for LDA-based Topic Analysis
Yi Yang; Jian Wang; Guigang Zhang; Weixing Huang
2016
Conference NameThe Second IEEE International Conference on Multimedia Big Data(IEEE BigMM 2016)
Conference Date20-22 April 2016
Conference PlaceCarleton University Ottawa, ON, Canada
Abstract

LDA-based topic analysis is widely used in text mining field. Considering the large scale of web documents, document clusters are usually analyzed instead of single ones. However, the existing visualizations of LDA-based clustering do not intuitively present contents of hot topics while maintaining the relationships between the topics and the document clusters. In this paper, we propose an integrated interactive visualization method that provides intuitive and effective views for topic popularity, topic contents, document clusters, and relationships between topics and document clusters. In this way, users can quickly identify the topic-based patterns. We show an experimental evaluation by comparing the tabular representation and our visualization. The results show that our method can significantly facilitate the topic analysis, particularly in the field of Chinese culture study.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23828
Collection中国科学院自动化研究所
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yi Yang,Jian Wang,Guigang Zhang,et al. An Interactive Visualization for LDA-based Topic Analysis[C],2016.
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