Topic modeling in marketing: recent advances and research opportunities

Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (2015) Introduction to mixed membership models and methods. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 3 Amado A, Cortez P, Rita P, Moro S (2017) Research trends on big data in marketing: a text mining and topic modeling based literature analysis. Eur Res Manag Bus Econ 24:1–7 Andrzejewski D, Zhu X (2009) Latent Dirichlet Alocation with Topic-in-Set Knowledge. SemiSupLearn’09. Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing. pp. 43–48. ISBN: 978-1-932432-38-1 Asuncion A, Welling M, Smyth P, The YW (2009) On smoothing and inference for topic models. pp. 27–34. https://arxiv.org/abs/1205.2662 Balasubramanyan R, Cohe WW (2015) Block-LDA: jointly modeling entity-annotated text and entity-entity links. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 255 Bart E (2009) Speeding up Gibbs sampling by variable grouping. NIPS Workshop on applications for topic models: text and beyond. https://www.parc.com/publication/3410/speeding-up-gibbs-sampling-by-variable-grouping.html Bartholomew DJ, Steele F, Moustaki I, Galbraith JI (2011) Analysis of multivariate social science data, 2nd edn. CRC Press, Taylor & Francis Group, Boca Raton, London Blanchard SJ, Aloise D, Desarbo WS (2017) Extracting summary piles from sorting task data. J Mark Res PrePrint, Unedited Blei DM (2012) Probabilistic topic models. Surveying a suite of algorithms that offer a solution to managing large document archives. Commun ACM 55(4):77–84 Blei DM (2014) Build, compute, critique, repeat: data analysis with latent variable models. Annu Rev Stat Appl 1:203–232. https://doi.org/10.1146/annurev-statistics-022513-115657 Blei DM, Jordan MI (2003a) Modeling annotated data. SIGIR’03. ACM 1581136463/03/0007 Blei DM, Lafferty JD (2006) Dynamic topic models. Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh, PA Blei DM, Lafferty JD (2007) A correlated topic model of science. Annu Appl Stat 1(1):17–35 Blei DM, Lafferty JD (2009) Topic models. In: Srivastava A, Sahami M (eds) Chapman and Hall/CRC. data mining and knowledge discovery series. Taylor and Francis Group, LLC, New York, p 71 Blei DM, McAuliffe JD (2010) Supervised topic models. NIPS Proceedings Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet Allocation. J Mach Learn Res 3(2003):993–1022 Blei DM, Carin L, Dunson D (2010) Probabilistic topic models: a focus on graphical model design and applications to document and image analysis. IEEE Signal Process Mag. https://doi.org/10.1109/msp.2010.938079 Boyd-Graber J, Mimno D, Newman D (2015) Care and feeding of topic models: problems, diagnostics, and improvements. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 225 Brett MR (2012) Topic modeling: a basic introduction. http://journalofdigitalhumanities.org/2-1/topic-modeling-a-basic-introduction-by-megan-r-brett/ Büschken J, Allenby GM (2016) Sentence-based text analysis for customer reviews. Market Sci 35(6):953–975 Büschken J, Allenby GM (2017) Improving text analaysis using sentence conjunctions and punctuation. SSRN. https://ssrn.com/abstract=2908915. Accessed 31 Jan 2017 Calheiros AC, Moro S, Rita P (2017) Sentiment classification of consumer-generated online reviews using topic modeling. J Hosp Market Manag 26(7):675–693. https://doi.org/10.1080/19368623.2017.1310075 Cao D, Ji R, Lin D, Li S (2014) Visual sentiment topic model based microblog image sentiment analysis. Multimed Tools Appl 2016(75):8955–8968. https://doi.org/10.1007/s11042-014-2337-z Chaney AJB, Blei DM (2012) Visualizing topic models. Assoc Adv Artif Intell. pp 419–422 Chang J, Blei DM (2009) Relational topic models for document networks. Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS). Vol. 5 of JMLR Chang J, Boyd-Graber J, Gerrish S, Wang C, Blei DM (2009) Reading tea leaves: how humans interpret topic models. http://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models.pdf. Accessed 28 July 2017 Chen AT, Sheble L, Eichler G (2013) Topic modeling and network visualization to explore patient experiences, pp 1–4. http://faculty.washington.edu/atchen/pubs/Chen_Sheble_Eichler_VAHC2013.pdf. Accessed 16 Jan 2017 Cho YS, Steeg GV, Galstyan A (2015) Mixed membership blockmodels for dynamic networks with feedback. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 527 Christidis K, Mentzas G (2013) A topic-based recommender system for electronic marketplace platforms. Expert Syst Appl 40:4370–4379 Clement M, Boßow-This S (2007) Fuzzy clustering mit Hilfe von mixture models. In: Albers S, Klapper D, Konradt U, Walter A, Wolf J. Methodik der empirischen Forschung. 2., überarbeitete und erweiterte Auflage. Gabler. pp 167–182 Costa Filho IG (2010) Mixture models for the analysis of gene expression: integration of multiple experimentsand cluster validation. Dissertation. University of Berlin Crain SP, Zhou K, Yang SH, Zha H (2012) Dimensionality reduction and topic modeling. In: Aggarwal CC, Zhai CX (eds) Mining text data. Springer, Heidelberg Dan N, Bellio R, Reutterer T (2017) A note on latent rating regression for aspect analysis of user-generated content. Working paper. Department of Marketing WU Vienna Do T-M-T, Gatica-Perez D (2010) By their apps you shall understand them: mining large-scale patterns of mobile phone usage. In: Proceedings of the 9th international conference on mobile and ubiquitous multimedia (MUM’10), 1–3 Dec. ISBN: 978-1-4503-0424-5 Fabrigar LR, Wegener DT (2012) Exploratory factor analysis. Understanding statistics. Oxford University Press, New York Flach PA (2001) On the state of the art in machine learning: a personal review. Artif Intell 131:199–222 Fox EB, Jordan MI (2015) Mixed membership models for time series. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 417 Frühwirth-Schnatter S (2006) Finite mixture and Markov switching models. Springer, New York Galyardt A (2015) Interpreting mixed membership. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 39 Gormley IC, Murphy TB (2015) Mixed membership models for rank data. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 461 Gormley MR, Dredze M, Van Durme B, Eisner J (2012) Shared components topic models. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp 783–792 Gross JH, Manrique-Vallier D (2015) A mixed membership approach to political ideology. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 117 Grün B, Hornik K (2011) Topicmodels: an R Package for fitting topic models. J Stat Softw 40:1–20 Ha T, Beijnon B, Kim S, Lee S, Kim JH (2017) Examining user perceptions of smartwatch through dynamic topic modeling. Telematics Inform 34:1262–1273 Han HJ, Mankad S, Gavirneni N, Verma R (2016) What guests really think of your hotel: text analytics of online customer reviews. Cornell Hosp Rep 16(2):3–17 Heinrich K (2015) Integration von Topic Models und Netzwerkanalyse bei der Bestimmung des Kundenwertes. In: Wissensgemeinsschaften 2015. Technische Universität Dresden. Verlag der Wissenschaften. Dresden Herzig J, Mass Y, Roitman H (2014) An author-reader influence model for detecting topic-based influencers in social media. ACM 978-1-4503-2954-5/14/09. HT’14, September 1–4 Hinton GE (2002) Training products of experts by minimizing contrastive divergence. GCNU TR 2000-004 Ho T, Do P (2015) Analyzing the changes in online community based on topic model and self-organizing map. IJACSA 6(7):100–108 Ho Q, Xing EP, Airoldi EM (2015) Analyzing time-evolving networks. In: Blei DM, Airoldi EM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 489 Hoffman MD, Blei DM, Bach F (2010) Online learning for latent Dirichlet allocation. NIPS Proceedings Hruschka H (2014) Linking multi-category purchases to latent activities of shoppers: analysing market baskets by topic models. Market ZFP 36(4):267–274 Hruschka H (2016) Hidden variable models for market basket data. University of Regensburg, Regensburg Hu B, Ester M (2013) Spatial topic modeling in online social media for location recommendation. RecSys ’13. ACM 978-1-4503-2409-0/13/10 Hu Z, Wang C, Yao J, Xing E, Yin H, Cui B (2013) Community specific temporal discovery from social media. arXiv:1312.0860v1. Accessed 3 Dec 2013 Iqbal HR, Ashraf MA, Nawab RMA (2015) Predicting an author’s demographics from text using topic modeling approach. Notebook for PAN at CLEF 2015 Ishingaki T, Ternui N, Sato T, Allenby GM (2015) Topic modeling of market responses for large-scale transaction data. Data science and service research discussion paper. Discussion paper No. 35. Center for Data Science and Service Research Graduate School of Economic and Management. Tohoku University Iwata T, Sawada H (2013) Topic model for analyzing purchase data with price Information. Data Min Knowl Disc 2013(26):559–573. https://doi.org/10.1007/s10618-012-0281-y Jacobs BJD, Donkers B, Fok D (2016) Model-based purchase predictions for large assortments. Market Sci 35(3):389–404 Jeong B, Yoon J, Lee JM (2017) Social media mining for product planning: a product opportunity mining approach based on topic modeling and sentiment analysis. Int J Inform Manag. https://doi.org/10.1016/j.ijinfomgt.2017.09.009 Jo Y, Oh A (2011) Aspect and sentiment unification model for online review analysis. WSDM’11. ACM 978-1-4503-0493-1/11/02 Kahn A, Baharudin B, Hong Lee L, Kahn K (2010) A review of machine learning algorithms for text-documents classification. J Adv Inform Technol 1(1):4–20 Karpienko R, Reutterer T (2017) An empirical study of journal positioning and the evolution of marketing subareas. Working paper. Department of Marketing WU Vienna Kjellin PE, Liu Y (2016) A survey on interactivity in topic models. IJACSA 7(4):456–461 Knights D, Mozer MC, Nocolov N (2009) Detecting topic drift with compound topic models. association for the advancement of artificial intelligence. www.aaai.org. Accessed 18 July 2018 Lakkaraju H, Bhattacharyya C, Merugu S, Bhattacharya I (2009) Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments. In: Proceedings of the 18th International Conference on World Wide Web (WWW 2009), pp 131–140 Le D-T, Nguyen C-T, Coltech Q-TH, Phan X-H, Horiguchi S (2008) Matching and ranking with hidden topics towards online contextual advertising. 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Lehmann DR (2005) Journal evolution and the development of marketing. J Public Policy Market 24(1):137–142 Liu L, Tang J, Han J, Jiang M, Yang S (2010) Mining topic-level influence in heterogeneous networks. CIKM’10, October 25–29, 2010, Toronto Lu B, Ott M, Cardi C, Tsou BK (2011) Multi-aspect sentiment analysis with topic models. 11th IEEE International Conference on Data Mining Workshops, pp 1–8 Luo J, Pan X, Zhu X (2015) Identifying digital traces for business marketing through topic probabilistic model. Technology analysis and strategic management. ISSN: 0953-7325 (Print) 1465-3990 (Online) Journal homepage. http://www.tandfonline.com/loi/ctas20. Accessed 24 Apr 2017 McLachlan GJ, Peel D (2000) Finite mixture models. Wiley, New York, Chichester, Weinheim, Brisbane, Singapore, Toronto. ISBN 0-471-00626-2 Melnykov V, Maitra R (2010) Finite mixture models and model-based clustering. Stat Surv 4:80–116 Mimno D, Wallach HM, Talley E, Leenders M, McCallum A (2011) Optimizing semantic coherence in topic models. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 262–272 Moghaddam S, Ester M (2012) On the design of LDA models for aspect-based opinion mining. In: Proceedings of the 21st ACM international conference on information and knowledge management (CIKM’12). ACM 978-1-4503-1156-4/12/10, pp 803–812 Mukherjee A, Liu B (2012) Aspect extraction through semi-supervised modeling. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pp 339–348 Muthen B (1978) Contributions to factor analysis of dichotomous variables. Psychometrika 43(4):551–560 Muthen B, Christoffersson A (1981) Simultaneous factor analysis of dichotomous variables in several groups. Psychometrika 46(4):407–419 Newman D, Karimi S, Cavedon L (2009) External evaluation of topic models. In: Proceedings of the 14th Australasian Document Computing Symposium. 4 Dec 2009 Newman D, Lau JH, Grieser K, Baldwin T (2010) Automatic evaluation of topic coherence. Human language technologies: The 2010 Annual Conference of the North American Chapter of the ACL. 100–108 Ngyen TH, Shirai K, Velcin J (2015) Sentiment analysis on social media for stock movement prediction. Expert Syst Appl 42(2015):9603–9611 Nimeroff J (2017) How machine learning will be used for marketing in 2017. Forbes technology council. Forbes. https://www.forbes.com/sites/forbestechcouncil/2017/03/10/how-machine-learning-will-be-used-for-marketing-in-2017/#74029c4e6d3d. Accessed 8 June 2017 Paisley J, Blei DM, Jordan MI (2015) Bayesian nonnegative matrix factorization with stochastic variational inference. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 205 Parasuraman A, Grewal D, Krishnan R (2007) Marketing research, 2nd edn. Houghton Mifflin Company, Boston Park KB, Ha SH (2016) Mining user-generated contents to detect service failures with topic model. Int J Comput Electr Autom Control Inform Eng 10(8):1491–1496 Pathak N, DeLong C, Banerjee A (2008) Social topic models for community extraction. The 2nd SNA-KDD Workshop’08 (SNA-KDD’08). ACM 978-1-59593-848-0 Paul M, Girju R (2009) Cross-cultural analysis of blogs and forums with mixed-collection topic models. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp 1408–1417, Singapore, 6–7 August 2009. c 2009 ACL and AFNLP Phuong DV, Phuong TM (2012) A keyword-topic model for contextual advertising. SoICT, pp 63–70. ACM 978-1-4503-1232-5/12/08. https://doi.org/10.1145/2350716.2350728 Proctor T (2005) Essentials of marketing research, 4th edn. Pearson Education Limited, Harlow Punj G, Stewart DW (1983) Cluster analysis in marketing research: review and suggestions for application. J Mark Res 20(2):134–148 Putri IR, Kusumaningrum R (2017) Latent Dirichlet allocation (LDA) for sentiment analysis toward tourism review in Indonesia. J Phys. https://doi.org/10.1088/1742-6596/801/1/012073 Rabinovich M, Blei DM (2014) The inverse regression topic model. In: Proceedings of the 31st International Conference on Machine Learning. JMLR: W&CP. Vol. 32 Rahman MdM, Wang H (2016) Hidden topic sentiment model. WWW 2016, pp 155–165. ACM 978-1-4503-4143-1/16/04 Ramage D, Dumais S, Liebling D (2010) Characterizing microblogs with topic models. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media Reutterer T (2003) Bestandsaufnahme und aktuelle Entwicklungen bei der Segmentierungsanalyse von Produktmärkten. J für Betriebswirtschaft 53(2):52–74 Roberts ME, Stewart BM, Tingley D (2015) Navigating the local modes of big data: the case of topic models. Draft. June 2015. Prepared for “Computational social science: discovery and prediction”, pp 1–55. https://scholar.harvard.edu/files/dtingley/files/multimod.pdf. Accessed 3 Jan 2018 Rosen-Zvi M, Griffiths T, Steyvers M, Smyth P (2004) The author-topic model for authors and documents. In: UAI ‘04 Proceedings of the 20th conference on Uncertainty in artificial intelligence. p 487–494 Salakhutdinov R, Hinton G (2009) Replicated softmax: an undirected topic model. Advances in neural information processing systems 22. NIPS Proceedings 2009 Sammut C, Webb GI (2011) Encyclopedia of machine learning. Springer, New York Schieber A, Hilbert A, Sommer S, Heinrich K (2011) Analyzing customer sentiments in microblogs—a topicmodel-based approach for Twitter datasets. In: Proceedings of the Seventeenth Americas Conference on Information Systems, Detroit, Michigan August 4th–7th Schmidt BM (2013) Words alone: dismantling topic models in the humanities. J Digit Humanit 2(1). http://journalofdigitalhumanities.org/2-1/words-alone-by-benjamin-m-schmidt/. Accessed 31 May 2017 Schröder N (2017) Using multidimensional item response theory models to explain multi-category purchases. Market ZFP 39(2):27–37. https://doi.org/10.15358/0344-1369-2017-2-27 Schröder N, Falke A, Hruschka H, Reutterer T (2017) Analyzing browsing and purchasing across multiple websites based on latent Dirichlet allocation. ALLDATA 2017. ISBN: 978-1-61208-552-4 Shafiei MM, Milios EE (2006) Latent Dirichlet co-clustering. In: Proceedings of the Sixth International Conference on Data Mining (ICDM’06). 0-7695-2701-9/06. pp 1–10 Shaw MJ, Subramaniam C, Tan GW, Welge ME (2001) Knowledge management and data mining for marketing. Decis Support Syst 31:127–137 Shringarpure S, Xing EP (2015) Population stratification with mixed membership models. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 397 Si J, Li Q, Qian T, Deng X (2014) Users’ interest grouping from online reviews based on topic frequency and order. WorldWideWeb 2014(17):1321–1342. https://doi.org/10.1007/s11280-013-0239-z Song L, Lau RYK, Kwok RCW, Mirkovski K, Dou W (2017) Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection. Electron Commer Res 17:51–81 Sun Y, Deng H, Han J (2012) Probabilistic models for text mining. In: Aggarwal CC, Zhai CX (eds) Mining text data. Springer, Heidelberg, pp 260–296 Sun F-T, Griss M, Mengshoel O, Yeh Y-T (2013) Latent topic analysis for predicting group purchasing behavior on the social web. http://repository.cmu.edu/cgi/viewcontent.cgi?article=1157&context=silicon_valley. Accessed 18 Apr 2017 Sweet TM, Thomas AC, Junker BW (2015) Hierarchical mixed membership stochastic blockmodels for multiple networks and experimental interventions. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 463 Tang J, Meng Z, Nguyen XL, Mei Q, Zhang M (2014) Understanding the limiting factors of topic modeling via posterior contraction analysis. In: Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014. JMLR: W&CP vol. 32 Teh YW, Jordan MI (2010) Hierarchical Bayesian nonparametric models with applications. In: Hjort NL, Holmes C, Müller P, Walker SG (eds) Bayesian Nonparametrics. Cambridge University Press, Cambridge, pp 158–207 Teh YW, Jordan MI, Beal MJ, Blei DM (2006) Hierarchical Dirichlet processes. J Am Stat Assoc 101(476):1566–1581. https://doi.org/10.1198/016214506000000302 Tirullinai S, Tellis GJ (2014) Mining marketing meaning from online chatter: strategic brand analysis of big data using latent Dirichlet allocation. J Market Res 51(4):463–479 Titov I, McDonald R (2008) Modeling online reviews with multi-grain topic models. WWW 2008. IW3C2. ACM 978-1-60558-085-2/08/04 Titterington D, Smith A, Makov U (1985) Statistical analysis of finite mixture distributions. Wiley. ISBN 0-471-90763-4 Tran T, Ho T, Do P (2015) Detecting communities and surveying the most influence of online users. ACSIJ 4(6):172–178 (ISSN: 2322-5157) Trusov M, MA L, Jamal Z (2016) Crumbs of the cookie: user profiling in customer-base analysis and behavioral targeting. Market Sci 35(3):405–426 Underwood T (2012) Topic modeling made just simple enough. https://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/ Wallach HM, Mimno D, McCallum A (2009) Rethinking LDA: Why priors matter. http://dirichlet.net/pdf/wallach09rethinking.pdf. Accessed 20 July 2017 Wang C, Blei DM (2011) Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’11). ACM 978-1-4503-0813-7/11/08, pp 448–456 Wang H, Lu Y, Zhai CX (2011) Latent aspect rating analysis without aspect keyword supervision. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’11). ACM 978-1-4503-0813-7/11/08, pp 618–626 Wang Y, Agichtein E, Benzi M (2012) TM-LDA: efficient online modeling of latent topic transitions in social media. KDD’12, August 12–16, 2012, Beijing, China. Copyright 2012 ACM 978-1-4503-1462-6/12/08 Wang Z, Li L, Zahng C, Huang Q (2015) Image-regulated graph topic model for cross-media topic detection. ICIMCS’15, August 19–21, 2015, Zhangjiajie, Hunan, China Wedel M (2002) Concomitant variables in finite mixture models. Stat Neerl 56(3):362–375 Wedel M, Kamakura WA (1999) Market segmentation, vol 2. Springer Science + Business Media, New York Welling M, Hinton G, Osindero S (2002) Learning sparse topographic representations with products of student t distributions. Advances in neural information processing systems. Vol. 15. Vancouver, Canada Weng J, Lim E-P, Jiang J, He Q (2010) TwitterRank: finding topic-sensitive influential twitterers. WSDM’10, February 4–6, 2010, New York City, New York, USA. Copyright 2010 ACM 978-1-60558-889-6/10/02, pp 261–270 Wood F, Perotte A (2015) Mixed membership classification for documents with hierarchically structured labels. In: Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (eds) Handbook of mixed membership models and their applications. CRC Press, Florida, p 305 Xie Y, Gao Y, Gou J, Cheng Y, Honbo D, Zhang K, Agrawal A, Choudhary A (2012) Probabilistic macro behavioral targeting. DUBMMSM’12, October 29, 2012, Maui, Hawaii, USA. ACM 978-1-4503-1707-8/12/10, pp 7–10 Yang Z, Kotov A, Mohan A, Lu S (2015) Parametric and non-parametric user-aware sentiment topic models. SIGIR’15, August 09–13, 2015, Santiago, Chile. ACM 978-1-4503-3621-5/15/08, pp 413–422 Yin Z, Cao L, Han J, Zhai C, Huang T (2011) Geographical topic discovery and comparison. WWW 2011 — session: spatio-temporal analysis. March 28–April 1, 2011, Hyderabad, India. ACM 978-1-4503-0632-4/11/03, pp 247–256 Zinman A, Fritz D (n.d.). Data portraiture and topic models. pp 1–5. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.4544. Accessed 31 Aug 2017