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Multi-modal multi-layered topic classification model for social event analysis

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Abstract

In this paper, we pay attention to reveal the event topics and track the evolutionary trend of social event and a novel probabilistic topic model is proposed. The Multi-modal Multi-layered Topic Classification Model (tm_MMC) for Social Event Analysis has the capacity for revealing visual and non-visual topics, by jointly modeling the textual and visual information while simultaneously learning and predicting the multi-layered category labels. In order to track the evolutionary trends of the topics online, tm_MMC uses topic intensity and heritability to incrementally build an up-to-date model. To evaluate the effectiveness of our model, we experiment using a collected data, and compare the results with those of other traditional models. The results demonstrate the effectiveness and advantages of our model against several state-of-the-art methods.

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References

  1. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2010) Slicsuperpixels. Tech. Rep, EPFL

  2. Adams RP, Ghahramani Z, Jordan MI (2010) Tree-structured stick breaking for hierarchical data. NIPS, pp 19–27

  3. Allan J (2002) Topic detection and tracking: Event-based information organization. Kluwer Academic Publishers, Boston

    Book  MATH  Google Scholar 

  4. Antoniak CE (1974) Mixtures of dirichlet processes with applications to bayesian nonparametric problems. Ann Stat 2(6):1152–1174

    Article  MathSciNet  MATH  Google Scholar 

  5. Bao Y, Collier N, Datta A (2013) A partially supervised cross-collection topic model for cross-domain text classification. ACM international conference on information and knowledge management, pp 239–248

  6. Barnard K, Duygulu P, Forsyth D, De Freitas N, Blei DM, Jordan MI (2003) Matching words and pictures. J Mach Learn Res 3(2):1107–1135

    MATH  Google Scholar 

  7. Becker H, Naaman M, Gravano L (2009) Event identification in social media. WebDB, pp 291–300

  8. Blei DM, Jordan MI (2003) Modeling annotated data. International ACM SIGIR conference on research and development in information retrieval, pp 127–134

  9. Blei DM, Mcauliffe JD (2010) Supervised topic models. Advances in Neural Information Processing Systems 3:327–332

    Google Scholar 

  10. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  11. Diakopoulos N, Naaman M, Kivran-Swaine F (2010) Diamonds in the rough: Social media visual analytics for journalistic inquiry. Visual analytics science and technology, pp 115–122

  12. Escalante HJ, Sucar LE, Montes M (2008) Late fusion of heterogeneous methods for multimedia image retrieval. ACM international conference on multimedia information retrieval, pp 172–179

  13. Gao H, Tang S, Zhang Y, Jiang D, Wu F, Zhuang Y (2012) Supervised Cross-collection Topic Modeling. ACM multimedia, pp 957–960

  14. Hatzivassiloglou V, Gravano L, Maganti A (2000) An investigation of linguistic features and clustering algorithms for topical document clustering, pp 224–231

  15. Hoffman MD, Blei DM, Bach FR (2010) Online learning for latent dirichlet allocation. Advances in neural information processing systems 23:856–864

    Google Scholar 

  16. Kim JH, Kim D, Kim S, Oh A (2012) Modeling topic hierarchies with the recursive Chinese restaurant process. ACM international conference on information and knowledge management, pp 783–792

  17. Kliegr T, Chandramouli K, Nemrava J, Svatek V, Izquierdo E (2008) Combining image captions and visual analysis for image concept classification. International Workshop on Multimedia Data Mining: Held in Conjunction with the ACM SIGKDD 29:10–16

    Google Scholar 

  18. Lacoste-Julien S, Sha F, Jordan MI (2008) Disclda: discriminative learning for dimensionality reduction and classification. Proceedings of NIPS neural information processing systems, pp 897–904

  19. Li C, Sun A, Datta A (2012) Twevent: segment-based event detection from tweets. ACM international conference on information and knowledge management, pp 155–164

  20. Li LJ, Socher R, Li FF (2009) Towards total scene understanding: classification, annotation and segmentation in an automatic framework. IEEE conference on computer vision and pattern recognition, pp 2036–2043

  21. Lin YR, Sundaram H, De Choudhury M, Kelliher A (2012) Discovering multirelational structure in social media streams. ACM Transactions on Multimedia Computing Communications and Applications 8(1):4

    Article  Google Scholar 

  22. Liu X, Huet B (2013) Heterogeneous features and model selection for Event-Based media classification. ACM international conference on multimedia retrieval, pp 151–158

  23. Makkonen J, Ahonen-Myka H, Salmenkivi M (2004) Simple semantics in topic detection and tracking. Information Retrieval Journal 7(3):347–368

    Article  MATH  Google Scholar 

  24. Mcminn AJ, Moshfeghi Y, Jose JM (2013) Building a large-scale corpus for evaluating event detection on twitter. ACM international conference on information and knowledge management, pp 409–418

  25. Minka TP (2003) Estimating a Dirichlet distribution. In UAI-2002 39(3273):115

    Google Scholar 

  26. Nguyen CT, Zhan DC, Zhou ZH (2013) Multi-modal image annotation with Multi-instance Multi-label LDA. International joint conference on artificial intelligence, pp 1558–1564

  27. Niu Z, Hua G, Gao X, Tian Q (2011) Spatial-discLDA for visual recognition. Comput Vis Pattern Recognit 42:1769–1776

    Google Scholar 

  28. Patel D, Hsu W, Lee ML (2008) Mining relationships among interval-based events for classi-fication. ACM SIGMOD international conference on management of data, pp 393–404. DBLP

  29. Pham TT, Maillot NE, Lim JH, Chevallet JP (2007) Latent semantic fusion model for image retrieval and annotation. 16th ACM conference on information and knowledge management, pp 439–444

  30. Putthividhy D, Attias HT, Nagarajan SS (2010) Topic regression multi-modal Latent Dirichlet Allocation for image annotation. Comput Vis Pattern Recognit 238:3408–3415

    Google Scholar 

  31. Qian S, Zhang T, Xu C (2014) Multi-modal supervised latent dirichlet allocation for event classification in social media, pp 152–157

  32. Radinsky K, Horvitz E (2013) Mining the web to predict future events. ACM international conference on web search and data mining, pp 255–264

  33. Ramage D, Hall D, Nallapati R, Manning CD (2009) Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. Conference on Empirical Methods in Natural Language Processing 1:248–256

    Google Scholar 

  34. Reuter T, Cimiano P (2012) Event-based classification of social media streams. ACM international conference on multimedia retrieval, pp 22–30

  35. Sang J, Xu C (2012) Right buddy makes the difference: an early exploration of social relation analysis in multimedia applications. ACM multimedia, pp 19–28

  36. Suhara Y, Toda H, Sakurai A (2008) Extracting related named entities from blogosphere for event mining. International conference on ubiquitous information management and communication, pp 225–229

  37. Teh YW, Jordan MI, Beal MJ, Blei DM (2006) Hierarchical dirichlet processes. J Am Stat Assoc 101(476):1566–1581

    Article  MathSciNet  MATH  Google Scholar 

  38. Wang X, Mccallum A (2006) Topics over time: a non-Markov continuous-time model of topical trends. 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 424–433

  39. Wang Y, Mori G (2009) Human action recognition by semilatent topic models. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(10):1762

    Article  Google Scholar 

  40. Wang C, Blei D, Li FF (2009) Simultaneous image classification and annotation. IEEE conference on computer vision and pattern recognition, pp 1903–1910

  41. Wang G, Hoiem D, Forsyth D (2009) Building text features for object image classification. IEEE computer society conference on computer vision and pattern recognition, pp 1367–1374

  42. Yang X, Zhang T, Xu C, Hossain MS (2015) Automatic visual concept learning for social event understanding. IEEE Transactions on Multimedia 17(3):346–358

    Article  Google Scholar 

  43. Zhang T, Xu C (2014) Cross-domain multi-event tracking via copmht. Acm Transactions on Multimedia Computing Communications and Applications 10(4):1–19

    Article  MathSciNet  Google Scholar 

  44. Zhang K, Zi J, Wu LG (2007) New event detection based on indexing-tree and named entity. SIGIR 2007: Proceedings of the international ACM SIGIR conference on research and development in information retrieval, pp 215–222

  45. Zhang T, Xu C, Zhu G, Liu S, Lu H (2012) A generic framework for video annotation via semi-supervised learning. IEEE Transactions on Multimedia 14 (4):1206–1219

    Article  Google Scholar 

  46. Zhang T, Ghanem B, Liu S, Xu C, Ahuja N (2013) Low-rank sparse coding for image classification. In: Proceedings of the IEEE international conference on computer vision, pp 281–288

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Acknowledgements

This work is supported by the National Natural Science Foundation of China No.61303131, No. 61672272, No. 60973040; Educational Reform Project of Fujian No.FBJG20170055.

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Authors and Affiliations

  1. College of Computer Science, Minnan Normal University, Zhangzhou, Fujian, 363000, China

    Y. H. Chen & Y. J. Lin

  2. Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou, China

    Y. H. Chen & Y. J. Lin

  3. School of Journalism and Communication, Minnan Normal University, Zhangzhou, Fujian, 363000, China

    C. Y. Yin

  4. College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China

    W. L. Zuo

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  1. Y. H. Chen
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  2. C. Y. Yin
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  4. W. L. Zuo
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Correspondence to Y. H. Chen.

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Chen, Y.H., Yin, C.Y., Lin, Y.J. et al. Multi-modal multi-layered topic classification model for social event analysis. Multimed Tools Appl 77, 23291–23315 (2018). https://doi.org/10.1007/s11042-017-5588-7

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  • DOI: https://doi.org/10.1007/s11042-017-5588-7

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