主题模型可视化研究综述
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摘要
在大数据背景下,以LDA为代表的主题模型数据挖掘技术得到了飞速的发展。随着研究的深入和科技工作者对结果可理解要求的提高,主题模型的可视化也成为了研究的热点。本文将将主题可视化分为两类:基于文档集内容的主题模型可视化和融合外部特征的主题模型可视化,本文在前人的基础上,总结了上述两类的主题模型可视化,分析了各个可视化工具的优缺点,并对其进行了客观的评价。也对以后的主题模型可视化提出了一些建议并进行了展望。
In the big data background, LDA topic model as one of the most popular technology of data mining has been rapid developed. With further research and scientists to improve the results understandable requirements, visualization of topic model has become a hot research. This article will devided the visualization of topic model into two categories: Visualization of topic model based on document and integration of external characteristics relating to topic model visualization, this article on the basis predecessors, summed up topic model above two types of visualization, analysis of the various visualization tools advantages and disadvantages, and its objective evaluation. Also give some suggestions and prospects about the future visualization of topic model relating to the future model visualization.
In the big data background, LDA topic model as one of the most popular technology of data mining has been rapid developed. With further research and scientists to improve the results understandable requirements, visualization of topic model has become a hot research. This article will devided the visualization of topic model into two categories: Visualization of topic model based on document and integration of external characteristics relating to topic model visualization, this article on the basis predecessors, summed up topic model above two types of visualization, analysis of the various visualization tools advantages and disadvantages, and its objective evaluation. Also give some suggestions and prospects about the future visualization of topic model relating to the future model visualization.
引文
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[21]范云满,马建霞.基于LDA与新兴主题特征分析的新兴主题探测研究[J].情报学报,2014,33(4):698-710.
[22]汤斯亮,程璐,邵健等.基于概率主题建模的新闻文本可视化综述[J].计算机辅助设计与图形学学报,2015(05):771-782.
[23]王晓光,程齐凯.基于NEViewer的学科主题演化可视化分析[J].情报学报,2013,32(9):900-911.
[24]崔凯,周斌,贾焰等.一种基于LDA的在线主题演化挖掘模型[J].计算机科学,2010(11):156-159.
[2]何锦群.LDA在信息检索中的应用研究[D]:天津:天津理工大学,2014.
[3]张小平,周雪忠,黄厚宽等.一种改进的LDA主题模型[J].北京交通大学学报,2010(02):111-114.
[4]邹晓辉,孙静.LDA主题模型[J].智能计算机与应用.2014,4(5):104-106.
[5]李勇,安新颖.基于LDA的主题演化研究[J].医学信息杂志,2013,34(2):57-61.
[6]施乾坤.基于LDA模型的文本主题挖掘和文本静态可视化的研究[D].广西:广西大学,2013:23-26.
[7]樊儒昆,傅晶,程司雷等.文本可视化研究综述。计算机辅助设计与图形学学报[J],2013,25(3):274-177.
[8]杨彦波,刘滨,祁明月.信息可视化研究综述[J].河北科技大学学报,2014,35(1):91-102.
[9]David M.Blei,John D.Lafferty.Visualizing Topics with Multi-Word Expressions[EB/OL].(2009-07-06)[2016-03-08].http://arxiv.org/abs/0907.1013v1.
[10]Chuang J,Manning C D,Heer J.Termite:visualization techniques for assessing textual topic models[C]//Proceedings of the International Working Conference on Advanced Visual Interfaces.New York:ACM Press,2012:74-77.
[11]Matthew J.Gardner,Jeff Lund,Joshua Lutes,Josh Hansen,Dan Walker,Eric Ringger,Kevin Seppi.The Topic Browser:An Interactive Tool for Browsing Topic Models[C].NIPS Workshop on Challenges of Data Visualization.USA:NIPS,2010:67-71.
[12]Carson Sievert,Kenneth E.Shirley.LDAvis:A method for visualizing and interpreting topics[J].Proceedings of the Workshop on Interactive Language Learning,Visualization,and Interfaces.2014:63-70.
[13]Chaney A J B,Blei D M.Visualizing topic models[C]//Proceedings of the 6th International AAAI Conference on Weblogs and Social Media.Palo Alto:AAAI Press,2012:419-422.
[14]Jaimie Murdock,Colin Allen.Visualization Techniques for Topic Model Checking[C].Association for the Advancement of Artificial Intelligence.2015.
[15]李湘东,张娇,袁满.基于LDA模型的科技期刊主题演化研究[J].情报杂志,2014(07):115-121.
[16]崔凯.基于LDA的主题演化研究与实现[D].北京:国防科学技术大学,2010:36-39.
[17]Susan Havre,Lucy Nowell.Theme River:Visualizing Thematic Changes in Large Document Collections[J],IEEE TRANSCAIONS O N V I S U A L I Z A T I O N A N D C O M P U T E R GRAPHICS.2014,8(1):9-19.
[18]张龙飞,姚中华,宋汉辰等.基于Theme River的可视化技术发展综述[J].系统仿真学报,2013(09):2091-2096.
[19]Furu Wei,Shixia Liu,Yangqiu Song,et al.TIARA:A Visual Exploratory Text Analytic System[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM Press,2010:153-162.
[20]Alsakran J,Chen Y,Luo D N,et al.Real-time visualization of streaming text with a force-based dynamic system[J].IEEE Computer Graphics and Applications,2012,32(1):34-45.
[21]范云满,马建霞.基于LDA与新兴主题特征分析的新兴主题探测研究[J].情报学报,2014,33(4):698-710.
[22]汤斯亮,程璐,邵健等.基于概率主题建模的新闻文本可视化综述[J].计算机辅助设计与图形学学报,2015(05):771-782.
[23]王晓光,程齐凯.基于NEViewer的学科主题演化可视化分析[J].情报学报,2013,32(9):900-911.
[24]崔凯,周斌,贾焰等.一种基于LDA的在线主题演化挖掘模型[J].计算机科学,2010(11):156-159.