Multi-modal Multi-view Topic-opinion Mining for Social Event Analysis

ABSTRACT

In this paper, we propose a novel multi-modal multi-view topic-opinion mining (MMTOM) model for social event analysis in multiple collection sources. Compared with existing topic-opinion mining methods, our proposed model has several advantages: (1) The proposed MMTOM can effectively take into account multi-modal and multi-view properties jointly in a unified and principled way for social event modeling. (2) Our model is general and can be applied to many other applications in multimedia, such as opinion mining and sentiment analysis, multi-view association visualization, and topic-opinion mining for movie review. (3) The proposed MMTOM is able to not only discover multi-modal common topics from all collections as well as summarize the similarities and differences of these collections along each specific topic, but also automatically mine multi-view opinions on the learned topics across different collections. (4) Our topic-opinion mining results can be effectively applied to many applications including multi-modal multi-view topic-opinion retrieval and visualization, which achieve much better performance than existing methods. To evaluate the proposed model, we collect a real-world dataset for research on multi-modal multi-view social event analysis, and will release it for academic use. We have conducted extensive experiments, and both qualitative and quantitative evaluation results have demonstrated the effectiveness of the proposed MMTOM.

References

1. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk. Slic superpixels, 2010. In Technical report, EPFL.
2. N. Akiva, E. Greitzer, Y. Krichman, and J. Schler. Mining and visualizing online web content using bam: Brand association map. In ICWSM, 2008.
3. Y. Bao, N. Collier, and A. Datta. A partially supervised cross-collection topic model for cross-domain text classification. In CIKM, pages 239--248, 2013.
4. D. Blei and J. McAuliffe. Supervised topic models. In NIPS, pages 121--128. 2008.
5. D. M. Blei and M. I. Jordan. Modeling annotated data. In SIGIR, pages 127--134, 2003.
6. D. M. Blei and J. D. Lafferty. Dynamic topic models. In ICML, pages 113--120, 2006.
7. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3:993--1022, 2003.
8. Q. Fang, C. Xu, J. Sang, M. S. Hossain, and M. Ghulam. Word-of-mouth understanding: Entity-centric multimodal aspect-opinion mining in social media. IEEE Trans. Multimedia, 17(12):2281--2296, 2015.
9. Y. Fang, L. Si, N. Somasundaram, and Z. Yu. Mining contrastive opinions on political texts using cross-perspective topic model. In WSDM ’12, pages 63--72, 2012.
10. T. L. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences, 101:5228--5235, 2004.
11. E. D. Gutiérrez, E. Shutova, P. Lichtenstein, G. de Melo, and L. Gilardi. Detecting cross-cultural differences using a multilingual topic model. TACL, 4:47--60, 2016.
12. T. Hofmann. Probabilistic latent semantic analysis. In UAI, pages 289--296, 1999.
13. K. L. Keller. Conceptualizing, measuring, and managing customer based brand equity. Journal of Marketing, pages 1--22, 1993.
14. A. O. H. I. Kevin M. Carter, Raviv Raich. Spherical laplacian information maps (slim) for dimensionality reduction. In SSP, 2009.
15. B. Liu and L. Zhang. A survey of opinion mining and sentiment analysis. In Mining Text Data, pages 415--463. 2012.
16. W. Min, B. Bao, and C. Xu. Multimodal spatio-temporal theme modeling for landmark analysis. IEEE MultiMedia, 21(3):20--29, 2014.
17. S. Moghaddam and M. Ester. Ilda: Interdependent lda model for learning latent aspects and their ratings from online product reviews. In SIGIR '11, pages 665--674, 2011.
18. S. Moghaddam and M. Ester. On the design of lda models for aspect-based opinion mining. In CIKM '12, pages 803--812, 2012.
19. M. Paul and R. Girju. Cross-cultural analysis of blogs and forums with mixed-collection topic models. In EMNLP '09, pages 1408--1417, 2009.
20. D. Putthividhya, H. T. Attias, and S. S. Nagarajan. Topic regression multi-modal latent dirichlet allocation for image annotation. In CVPR, pages 3408--3415, 2010.
21. S. Qian, T. Zhang, R. Hong, and C. Xu. Cross-domain collaborative learning in social multimedia. In Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, MM '15, pages 99--108, 2015.
22. S. Qian, T. Zhang, C. Xu, and M. S. Hossain. Social event classification via boosted multimodal supervised latent dirichlet allocation. TOMCCAP, 11(2):27:1--27:22, 2014.
23. M. Qiu and J. Jiang. A latent variable model for viewpoint discovery from threaded forum posts. In In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics, pages 1031--1040, 2013.
24. D. Ramage, P. Heymann, C. D. Manning, and H. Garcia-Molina. Clustering the tagged web. In WSDM. ACM, 2009.
25. X. Yang, T. Zhang, and C. Xu. Cross-domain feature learning in multimedia. IEEE Trans. Multimedia, 17(1):64--78, 2015.
26. C. Zhai, A. Velivelli, and B. Yu. A cross-collection mixture model for comparative text mining. In KDD '04, pages 743--748, 2004.
27. T. Zhang, B. Ghanem, S. Liu, C. Xu, and N. Ahuja. Low-rank sparse coding for image classification. In ICCV, pages 281--288, 2013.
28. T. Zhang, S. Liu, N. Ahuja, and M.-H. Yang. Robust visual tracking via consistent low-rank sparse learning. International Journal of Computer Vision, 111(2):171--190, 2015.

Index Terms

• Information systems

  • Information retrieval

    • Document representation

      • Document topic models