Topic evolution based on the probabilistic topic model: a review

Abstract

Accurately representing the quantity and characteristics of users’ interest in certain topics is an important problem facing topic evolution researchers, particularly as it applies to modern online environments. Search engines can provide information retrieval for a specified topic from archived data, but fail to reflect changes in interest toward the topic over time in a structured way. This paper reviews notable research on topic evolution based on the probabilistic topic model from multiple aspects over the past decade. First, we introduce notations, terminology, and the basic topic model explored in the survey, then we summarize three categories of topic evolution based on the probabilistic topic model: the discrete time topic evolution model, the continuous time topic evolutionmodel, and the online topic evolution model. Next, we describe applications of the topic evolution model and attempt to summarize model generalization performance evaluation and topic evolution evaluation methods, as well as providing comparative experimental results for different models. To conclude the review, we pose some open questions and discuss possible future research directions.

Keywords

topic evolution / probabilistic topic models / text corpora / evaluation method

References

[1] AllanJ. Introduction to topic detection and tracking. Topic Detection and Tracking. The Information Retrieval Series, Vol 12. SpringerUS, 2002, 1–16

[2] AllanJ, Carbonell J G, DoddingtonG , YamronJ, YangY. Topic detection and tracking pilot study final report. In: Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop. 1998, 194–218

[3] NallapatiR, FengA, PengF, Allan J. Event threading within news topics. In: Proceedings of the 13th ACM International Conference on Information and Knowledge Management. 2004, 446–453

[4] MorinagaS, Yamanishi K. Tracking dynamics of topic trends using a finite mixture model. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004, 811–816

[5] KumarR, Mahadevan U, SivakumarD . A graph-theoretic approach to extract storylines from search results. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004, 216–225

[6] LinF R, HuangF M, LiangC H. Individualized storyline-based news topic retrospection. In: Proceedings of Pacific Asia Conference on Information Systems: Managing Diversity in Digital Enterprises. 2007

[7] AhmedA, HoQ, TeoC H, Eisenstein J, SmolaA J , XingE P. Online inference for the infinite topic-cluster model: storylines from streaming text. In: Proceedings of the International Conference on Artificial Intelligence and Statistics. 2011, 101–109

[8] HofmannT. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 2001, 42(1): 177–196

[9] BleiD M, NgA Y, JordanM I. Latent dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993–1022

[10] ShanB, LiF. A survey of topic evolution based on LDA. Journal of Chinese Information Processing, 2010, 24(1): 43–49

[11] ElshamyW. Continuous-time infinite dynamic topic models. Dissertation for the Doctoral Degree. Manhattan: Kansas State University, 2013

[12] DaudA, LiJ Z, ZhouL Z, Muhammad F. Knowledge discovery through directed probabilistic topic models: a survey. Frontiers of Computer Science in China, 2010, 4(2): 280–301

[13] SteyversM, Griffiths T. Probabilistic topic models. Handbook of Latent Semantic Analysis, 2007, 427(2): 424–440

[14] BleiD M, Lafferty J D. Dynamic topic models. In: Proceedings of the 23rd ACM International Conference on Machine Learning. 2006, 113–120

[15] BleiD M, Lafferty J D. A correlated topic model of science. Annals of Applied Statistics, 2007, 1(1): 17–35

[16] BleiD M, Griffiths T L, JordanM I . The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 2010, 57(2): 7

[17] BleiD M, CarinL, DunsonD. Probabilistic topic models. IEEE Signal Processing Magazine, 2010, 27(1): 55–65

[18] BleiD M. Probabilistic topic models. Communications of the ACM, 2012, 55(4): 77–84

[19] XingE P. On topic evolution. Technical Report CMU-CALD-05-115. 2005

[20] TehY W, JordanM I, BealM J, Blei D M. Hierarchical dirichlet processes. Journal of the American Statistical Association, 2006, 101: 1566–1581

[21] MeiQ Z, ZhaiC X. Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2005, 198–207

[22] NallapatiR M, Ditmore S, LaffertyJ D , UngK. Multiscale topic tomography. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2007, 520–529

[23] AhmedA, XingE P. Dynamic non-parametric mixture models and the recurrent Chinese restaurant process with application to evolutionary clustering. In: Proceedings of the SIAM International Conference on Data Mining. 2008, 219–230

[24] AhmedA, XingE P. Timeline: dynamic hierarchical Dirichlet process model for recovering birth/death and evolution of topics in text stream. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence. 2010, 20–29

[25] WangJ, LiuX H, WangJ L, Zhao W D. News topic evolution tracking by incorporating temporal information. Communications in Computer and Information Science, 2014, 496(12): 465–472

[26] WangX R, McCallum A. Topics over time: a non-markov continuoustime model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006, 424–433

[27] WangC, BleiD, HeckermanD. Continuous time dynamic topic models. In: Proceedings of the International Conference on Uncertainty in Artificial Intelligence. 2008, 579–586

[28] KawamaeN. Trend analysis model: trend consists of temporal words, topics, and timestamps. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 317–326

[29] DubeyA, HefnyA, WilliamsonS , XingE P. A nonparametric mixture model for topic modeling over time. In: Proceedings of the SIAM International Conference on Data Mining. 2013, 530–538

[30] LiF F, PeronaP. A Bayesian hierarchical model for learning natural scene categories. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 524–531

[31] CaniniK P, ShiL, GriffithsT L . Online inference of topics with latent Dirichlet allocation. In: Proceedings of the International Conference on Artificial Intelligence and Statistics. 2009, 65–72

[32] HoffmanM, BachF R, BleiD M. Online learning for latent dirichlet allocation. In: Proceedings of the Neural Information Processing Systems Conference. 2010, 856–864

[33] SatoI, Kurihara K, NakagawaH . Deterministic single-pass algorithm for LDA. In: Proceedings of the Neural Information Processing Systems Conference. 2010, 2074–2082

[34] AlSumaitL, Barbará D, DomeniconiC . On-line LDA: adaptive topic models for mining text streams with applications to topic detection and tracking. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 3–12

[35] Gohr, A, Hinneburg A, SchultR , SpiliopoulouM. Topic evolution in a stream of documents. In: Proceedings of the SIAM International Conference on Data Mining. 2009, 859–870

[36] IwataT, YamadaT, SakuraiY, Ueda N. Online multiscale dynamic topic models. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and DataMining. 2010, 663–672

[37] AhmedA, HoQ, EisensteinJ , XingE, SmolaA J, TeoC H. Unified analysis of streaming news. In: Proceedings of the 20th International Conference on World Wide Web. 2011, 267–276

[38] GriffithsT L, Steyvers M. Finding scientific topics. Proceedings of the National Academy of Sciences, 2004, 101(suppl 1): 5228–5235

[39] HallD, Jurafsky D, ManningC D . Studying the history of ideas using topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2008, 363–371

[40] BolelliL, Ertekin , GilesC L . Topic and trend detection in text collections using latent dirichlet allocation. In: Proceedings of the European Conference on Information Retrieval. 2009, 776–780

[41] SteyversM, SmythP, Rosen-ZviM, Griffiths T. Probabilistic authortopic models for information discovery. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004, 306–315

[42] Rosen-ZviM, Griffiths T, SteyversM , SmythP. The author-topic model for authors and documents. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. 2004, 487–494

[43] NallapatiR M, AhmedA, XingE P, Cohen W W. Joint latent topic models for text and citations. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008, 542–550

[44] ZhouD, JiX, ZhaH Y, Giles C L. Topic evolution and social interactions: how authors effect research. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management. 2006, 248–257

[45] HeQ, ChenB, PeiJ, Qiu B J, MitraP , GilesL. Detecting topic evolution in scientific literature: how can citations help? In: Proceedings of the 18th ACM Conference on Information and Knowledge Management. 2009, 957–966

[46] WangX L, ZhaiC X, RothD. Understanding evolution of research themes: a probabilistic generative model for citations. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013, 1115–1123

[47] WangX H, ZhaiC X, HuX, SproatR. Mining correlated bursty topic patterns from coordinated text streams. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2007, 784–793

[48] HongL J, DomB, GurumurthyS , TsioutsiouliklisK. A timedependent topic model for multiple text streams. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011, 832–840

[49] LinC X, ZhaoB, MeiQ Z, Han J W. PET: a statistical model for popular events tracking in social communities. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010, 929–938

[50] LinC X, MeiQ Z, HanJ W, Jiang Y L, DanilevskyM . The joint inference of topic diffusion and evolution in social communities. In: Proceedings of the 11th IEEE International Conference on Data Mining. 2011, 378–387

[51] TangX N, YangC C. TUT: a statistical model for detecting trends, topics and user interests in social media. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012, 972–981

[52] SasakiK, Yoshikawa T, FuruhashiT . Online topic model for twitter considering dynamics of user interests and topic trends. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2014, 1977–1985

[53] IwataT, Watanabe S, YamadaT , UedaN. Topic tracking model for analyzing consumer purchase behavior. In: Proceedings of the International Joint Conference on Artificial Intelligence. 2009, 1427–1432

[54] CaiG Y, PengL B, WangY. Topic detection and evolution analysis on microblog. In: Shi Z Z, Wu Z H, Leake D, et al. eds. Intelligent Information Processing VII. IFIP Adrances in Information and Communication Technology, Vol 432. Berlin: Springer, 2014, 67–77

[55] WallachH M, MurrayI, SalakhutdinovR , MimnoD. Evaluation methods for topic models. In: Proceedings of the 26th Annual International Conference on Machine Learning. 2009, 1105–1112

[56] SahaA, Sindhwani V. Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining. 2012, 693–702

[57] VacaC K, Mantrach A, JaimesA , SaerensM. A time-based collective factorization for topic discovery and monitoring in news. In: Proceedings of the 23rd ACM International Conference on World Wide Web. 2014, 527–538

[58] ChenY, ZhangH, WuJ J, Wang X G. Modeling emerging, evolving and fading topics using dynamic soft orthogonal NMF with sparse representation. In: Proceedings of the IEEE International Conference on Data Mining. 2015, 61–70

[59] GlobersonA, Chechik G, PereiraF , TishbyN. Euclidean embedding of co-occurrence data. The Journal of Machine Learning Research, 2007, 8(4): 2265–2295

[60] ChangJ, Boyd-Graber J L, GerrishS , WangC, BleiD M. Reading tea leaves: how humans interpret topic models. In: Proceedings of the Neural Information Processing Systems Conference. 2009, 288–296

[61] WallachH M. Topic modeling: beyond bag of words. In: Proceedings of the 23rd International Conference on Machine Learning. 2006, 977–984

RIGHTS & PERMISSIONS

2017 Higher Education Press and Springer-Verlag Berlin Heidelberg