Visual Analysis of Topic Transition among Different Sources of Text Corpora

被引：0
|
作者：
Zhang Y. [1 ]
Shao Y. [1 ]
Zhang J. [1 ]
机构：
[1] School of Computer Software, Tianjin University, Tianjin
来源：
| 1600年 / Institute of Computing Technology卷 / 29期
关键词：
Text and document data; Time series data; Topic visualization; Visualization in social and information sciences; Visualization system and toolkit design;
D O I：
10.3724/SP.J.1089.2017.16634
中图分类号：
学科分类号：
摘要：
Traditional news media and social media may convey different perspectives and topics may affect each other in the same event. This paper presents a set of visual analysis methods to analyze the similarities and differences in comments of event on multi text sources and the topic transition pattern over time. First, an information transition model based on topic analysis is proposed. LDA model was used to extract topics and the transmission relationships between topics are analyzed by content correlation and temporal correlation. An approach which combine Sankey graph and timeline technique is applied to visualize the topic transition model. The topic hierarchy view, word distance view and raw data view are used to help users comprehensive understanding the topics. Finally, this paper provides a visual analysis system prototype. The event of "South Korea deployed THAAD" is provided to verify the usability and effectiveness of the visual analysis system. © 2017, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
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页码：2265 / 2272
页数：7
相关论文
共 24 条
  • [1] Wu Y., Cao N., Gotz D., Et al., A survey on visual analytics of social media data, IEEE Transactions on Multimedia, 18, 11, pp. 2135-2148, (2016)
  • [2] Zubiaga A., Ji H., Knight K., Curating and contextualizing Twitter stories to assist with social newsgathering, Proceedings of the 2013 International Conference on Intelligent User Interfaces, pp. 213-224, (2013)
  • [3] Diakopoulos N., Naaman M., Kivran-Swaine F., Diamonds in the rough: social media visual analytics for journalistic inquiry, Proceedings of the Visual Analytics Science and Technology (VAST), pp. 115-122, (2010)
  • [4] Chae J., Thom D., Bosch H., Et al., Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition, Proceedings of the Visual Analytics Science and Technology (VAST), pp. 143-152, (2012)
  • [5] Wang F., Chen W., Tao Y., Et al., A search system for social media based on visual interface, Journal of Computer- Aided Design & Computer Graphics, 27, 3, pp. 460-468, (2015)
  • [6] Wang X., Liu S., Liu J., Et al., TopicPanorama: a full picture of relevant topics, IEEE Transactions on Visualization and Computer Graphics, 22, 12, pp. 2508-2521, (2016)
  • [7] Leskovec J., Backstrom L., Kleinberg J., Meme-tracking and the dynamics of the news cycle, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 497-506, (2009)
  • [8] Liu S., Chen Y., Wei H., Et al., Exploring topical lead-lag across corpora, IEEE Transactions on Knowledge and Data Engineering, 27, 1, pp. 115-129, (2015)
  • [9] Wang X., Liu S., Chen Y., Et al., How ideas flow across multiple social groups, Proceedings of the Visual Analytics Science and Technology (VAST), pp. 51-60, (2016)
  • [10] Sun G.D., Wu Y.C., Liang R.H., Et al., A survey of visual analytics techniques and applications: state-of-the-art research and future challenges, Journal of Computer Science and Technology, 28, 5, pp. 852-867, (2013)
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