Computer Science ›› 2017, Vol. 44 ›› Issue (2): 46-55.doi: 10.11896/j.issn.1002-137X.2017.02.005

Previous Articles     Next Articles

Survey on Temporal Topic Model Methods and Application

GUI Xiao-qing, ZHANG Jun, ZHANG Xiao-min and YU Peng-fei   

  • Online:2018-11-13 Published:2018-11-13

Abstract: With the fast development of Internet,the data has reached an unprecedented scale.However,it is becoming more and more difficult to get valuable information from mass data.Topic model is a new probabilistic model which has been widely applied in natural language processing,text mining,information retrieval and other fields in recent years.The technology of topic detecting and temporal analysis can help users focus on interested information.Temporal topic model has gradually become a hot research topic in the field of computer science.Therefore,temporal topic model and its application were investigated in detail in this paper.Firstly,the basic knowledge of topic model and temporal topic model were introduced.Secondly,temporal models were categorized into several types,representative models were discussed and their advantages and disadvantages were also analyzed.Thirdly,the applications of temporal models were summarized in several fields.Finally,the future development trends of temporal topic models were presented.

Key words: Temporality,Topic model,Temporal topic model

[1] MBLEI D,Ng A Y,JORDAN M I.Latent Dirichlet Allocation[J].Journal of Machine Learning Research,2003(3):993-1022.
[2] CHEN B.Topic Oriented Evolution and Sentiment Analysis[D].The Pennsylvania State University,2011.
[3] DEERWESTER S,DUMAIS S T,FURNAS G W,et al.Indexing by Latent Semantic Analysis [J].Journal of the American Society for Information Science,1990,41(6):391-407.
[4] HOFMANN T.Probabilistic Latent Semantic Indexing [C]∥Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,1999:50-57.
[5] BLEI D M,LAFFERTY J D.Topic models In Text Mining:Classification Clustering and Applications[M].Chapman & Hall,London,UK,2009:71-94.
[6] WANG L.The Research of Dynamic Network Community Detection Algorithm[D].Shenyang:Northeastern University.2013 .(in Chinese) 王玲.动态网络社区发现算法研究[D].沈阳:东北大学,2013.
[7] BLEI D M,LAFFERTY J D.Dynamic Topic Models [C]∥Proceedings of the 23rd International Conference on Machine Lear-ning.ACM,2006:113-120.
[8] CUI K.The Research and Implementation of Topic EvolutionBased on LDA[D].Changsha:National University of Defense Technology,2010.(in Chinese) 崔凯.基于LDA的主题演化研究与实现[D].长沙:国防科学技术大学,2010.
[9] XU G,WANG H F.The Development of Topic Models in Natural Language Processing[J].Chinese Journal of Computers,2011,34(8):1423-1436.(in Chinese) 徐戈,王厚峰.自然语言处理中主题模型的发展[J].计算机学报,2011,34(8):1423-1436.
[10] ROSEN-ZVI M,GRIFFITHS T,STEYVERS M,et al.The Author Topic Model for Authors and Documents [C]∥Procee-dings of the 20th Conference on Uncertainty in Artificial Intelligence.AUAI Press,2004.
[11] BLEI D M,LAFFERTY J D.Correlated Topic Models [C]∥Proceedings of the 23rd International Conference on Machine Learning.2006.
[12] NAVEED N,SIZOV S,STAAB S.ATT:Analyzing Temporal Dynamics of Topics and Authors in Social Media[C]∥Procee-dings of the 3rd International Web Science Conference.ACM,2011.
[13] BLEI D,MCAULIFFE J.Supervised topic models[J].Advances in Neural Information Processing Systems ,2010,3:327-332.
[14] MCCALLUM A,CORRADA-EMMANUEL A,WANG X.TheAuthor-Recipient-Topic Model for Topic and Role Discovery in Social Networks:Experiments with Enron and Academic Email.http://ciir-publications.cs.umass.edu/pdf/IR-381.pdf .
[15] NALLAPTI R,COHEN W.Link-PLSA-LDA:A New Unsupervised Model for Topics and Influence in Blogs[C]∥Proceedings of the International Conference for Weblogs and Social Media.Seattle,Washington,USA,2008.
[16] RAMAGE D,HALL D,NALLAPATI R,et al.Labeled LDA:A supervised topic model for credit attribution in multi-labeled corpora[C]∥Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing.Singapore,2009:248-256.
[17] WANG X R,MCCALLUM A.Topics over Time:A Non-MarkovContinuous-Time Model of Topical Trends[C]∥Proceedings of the 12th ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining.New York,NY,USA,ACM,2006:424-433.
[18] ALSUMAIT L,BARBAR D,DOMENICONI C.On-Line LDA:Adaptive Topic Models for Mining Text Streams withApplications to Topic Detection and Tracking [C]∥Proceedings of the 8th IEEE International Conference on Data Mining.IEEE,2008:3-12.
[19] ZHAO W X,JIANG J,WENG J,et al.Comparing Twitter and Traditional Media Using Topic Models[M]∥Advances in Information Retrieval.Springer Berlin Heidelberg,2011:338-349.
[20] CHANG J,BLEI D M.Relational Topic Models For Document Networks[C]∥AISTATS.2009.
[21] LI Dai-feng,DING Ying,XIN Shuai,et al.Adding Community and Dynamic to Topic Models[J].Journal of Informetrics,2012,6(2):237-253.
[22] LIAO J H,SUN K Y,ZHONG L X.Study on a Hot Topics Analysis System based on Time Sliced Topic Model[J].Library and Information Service,2013,57(9):96-102.(in Chinese) 廖君华,孙克迎,钟丽霞.一种基于时序主题模型的网络热点话题演化分析系统[J].图书情报工作,2013,57(9):96-102 .
[23] DING W,CHEN C.Dynamic Topic Detection and Tracking:A Comparison of HDP,C-word,and Co-citation Methods[J].Journal of the Association for Information Science and Techno-logy,2015,65(10):2084-2097.
[24] FAN Y M,MA J X.Review on the LDA-based Techniques Detection for the Field Emerging Topic[J].New Technology of Library and Information Service,2012(12):58-65.(in Chinese) 范云满,马建霞.利用LDA的领域新兴主题探测技术综述[J].现代图书情报技术,2012(12):58-65.
[25] WEI X,SUN J,WANG X.Dynamic Mixture Models for Multiple Time-Series [C]∥Proceedings of the 20th International Joint Conference on Artificial Intelligence.Hyderabad,India,2007:2909-2914.
[26] WANG C,BLEI D,HECKERMAN D.Continuous Time Dy-namic Topic Models [M]∥D Mallester A Nicholson,Uncertainty in Artificial Intelligence.2012:579-586.
[27] TEH Y W.Dirichlet processes[M]∥Encyclopedia of Machine Learning.Springer,2010.
[28] TEH Y W,JORDAN M I,BEAL M J,et al.HierachicalDirichlet process[J].Journal of the American Statiscal Association,2006,101(476):1566-1581.
[29] AHMED A,XING E P.Dynamic Non-Parametric Mixture Mo-dels and the Recurrent Chinese Restaurant Process:With Applications to Evolutionary Clustering[C]∥Proceedings of the SIAM International Conference on Data Mining.Atlanta,Georgia,USA,2008:219-230.
[30] AHMED A,XING E P.Timeline:A Dynamic HierarchicalDirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream [C]∥Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence.AUAI Press,2010.
[31] ZHAO X,LI X M.The Application of Text Mining Based on Topic Model[D].Beijing:Peking University,2011.(in Chinese) 赵鑫,李晓明.主题模型在文本挖掘中的应用[D].北京:北京大学,2011.
[32] ELSHAMY W S.Continuous-time Infinite Dynamic Topic Mo-dels [D].Manhattan,Kansas:Kansas State University,2013.
[33] JIANG Z R,CHEN Y,GAO L C,et al.A Supervised Dynamic Topic Mode[J].Acta Scientiarum Naturalium University Pekinensis,2015,51(2):367-376.(in Chinese) 蒋卓人,陈燕,高良才,等.一种结合有监督学习的动态主题模型[J].北京大学学报,2015,51(2):367-376.
[34] ALSUMAIT L,BARBAR D,Domeniconi C.On-Line LDA:A-daptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking [C]∥Proceedings of the 8th IEEE International Conference on Data Mining.IEEE,2008:3-12.
[35] LI D F,YING D,XIN S,et al.Adding Community and Dynamic to Topic Models[J].Journal of Informetrics,2012,6(2):237-253.
[36] KUMAR R,RAGHAVAN N P,TOMKINS A.On the Bursty Evolution of Blogspace[C]∥Proceedings of the International Conference on World Wide Web.2003.
[37] ZHAO W X,JIANG J,WENG J S,et al.Comparing twitter and traditional media using topic models[C]∥ECIR.2011:338-349.
[38] DUBEY A,HEFNY A,WILLIAMSON S,et al.A non-parametric mixture model for topic modeling over time (2012)[J].arXiv preprint arXiv:1208.4411.
[39] YIN H Z,CUI B,LU H,et al.A Unified Model for Stable and Temporal Topic Detection from Social Media Data[C]∥IEEE International Conference on Data Engineering.2013:661-672.
[40] HE Q,CHEN B,PEI J,et al.Detecting topic evolution in scientific literature:how can citations help?[C]∥Proceeding of the 18th ACM Conference on Information Andknowledge Ma-nagement(CIKM’09).New York,NY,USA,2009:957-966.
[41] ZHOU D,JI X,ZHA H,et al.Topic Evolution and Social Interactions:How Authors Effect Research[C]∥Proceedings of the 15thInternational Conference on Information and Knowledge Management.2006:248-257
[42] CHEN B.Topic oriented evolution and sentiment analysis[D].PA,USA:Tennsylvania State University,2011.
[43] YAN J.Research on Community Discovery Based on TopicModel[D].Chengdu:Southwest University,2012.(in Chinese) 严姣.基于主题模型的社区发现研究[D].成都:西南大学,2012.
[44] 毕娟,秦志光,黄嘉.Dynamic Topic Model for Detecting Community in Social Networks[C]∥全国博士生学术年会.2013.
[45] WAINWRIGHT M J,JORDAN M I.Graphical Models,Exponential Families,and Variational Inference[J].Foundationa and Trends in Machine Learning,2008,1(1/2):1-305.
[46] GRIFFITHS T.Gibbs Sampling In the Generative Model of Latent Dirichlet Allocation.http://www-psych.stanfor-dedu/~gruffydd/cogsci02/1.
[47] MIMNO D,WALLACH H,MACLLUM A.Gibbs sampling for logistic normal topic models with graph-based priors[C]∥Proceedings of the NIPS Workshop on Analyzing Graphs.Whistler,Canada,2008.
[48] NALLAPATI R,COHEN W,LAFFERTY J.Parallelized variational EM for latent dirichlet allocation:An experimental eva-luation of speed and scalability[C]∥Proceedings of the ICDM Workshop on High Performance Data Mining.Omaha,USA,2007:349-354.
[49] WALLACH H M,MURRAY I,SALAKHUTDINOV R,et al.Evaluation methods for topic models[C]∥ICML.2009:1105-1112.
[50] HONG L,YIN D,GUO J,et al.Tracking trends:incorporating term volume into temporal topic models[C]∥Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2013:484-492.
[51] BUNTINE W.Estimating Likelihoods for Topic Models[M]∥Advances in Machine Learning.Springer Berlin Heidelberg,2009:51-64.
[52] CHUA F C T,OENTARYO R J,LIM E P.Using Linear Dynamical Topic Model for Inferring Temporal Social Correlation in Latent Space[J].Computer Science,2015,6(19):189-221
[53] XU G B,DENG W.Music classification method combiningDCTM and HMM[J].Computer Engineering and Design,2012,3(11):4245-4249.(in Chinese) 徐桂彬,邓伟.结合DCTM与HMM的音乐分类方法[J].计算机工程与设计,2012,3(11):4245-4249.
[54] YUAN L,ZHANG L B.Applying Temporal Features of Social Tags to Tag Predication[J].Computer Science,2012,9(6):179-183.(in Chinese) 袁柳,张龙波.标签时态特征分析及其在标签预测中的应用[J].计算机科学,2012,9(6):179-183.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .