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2016 IEEE Second International Conference on Multimedia Big Data (BigMM) (2016)
Taipei, Taiwan
April 20, 2016 to April 22, 2016
ISBN: 978-1-5090-2180-2
pp: 25-32
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.
INDEX TERMS
Conferences, Multimedia communication, Big data
CITATION

Y. Yang, J. Wang, W. Huang and G. Zhang, "TopicPie: An Interactive Visualization for LDA-Based Topic Analysis," 2016 IEEE Second International Conference on Multimedia Big Data (BigMM)(BIGMM), Taipei, Taiwan, 2016, pp. 25-32.
doi:10.1109/BigMM.2016.25
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