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Multi-opinion Ring: visualizing and predicting multiple opinion orientations in online social media

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Abstract

In the era of the Social Web, actors (e.g. people, organizations, nations, etc) of online social media often voice out their opinions towards a variety of opinion targets. Extracting and visualizing distributions of multiple opinions among actors facilitates individuals or organizations to extract valuable social intelligence from online social media. The main contribution of our research reported in this paper is the development of a novel opinion analysis methodology named Multi-opinion Ring for visualizing and predicting multiple opinion orientations held by different groups of actors in online social media. In particular, the proposed Multi-opinion Ring method combines visualization techniques with machine learning methods to predict the opinion inclinations of actors who are originally neutral to different opinion targets. A series of controlled experiments, user-based evaluations, and case studies show that the proposed Multi-opinion Ring method significantly outperforms classical visualization methods in terms of the cohesiveness of the graphical layout and the informativeness of the visualized contents.

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References

  1. Bagheri A, Saraee M, De Jong F (2013) Care more about customers: unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowl-Based Syst 52:201–213

    Article  Google Scholar 

  2. Bo P, Lee L (2008) Opinion mining and sentiment analysis. Foundations and trends in information retrieval 2(1–2):1–135

    Google Scholar 

  3. Bo P, Lee L, Vaithyanathan S (2002) Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Vol 10. Association for Computational Linguistics, pp 79–86

  4. Brandes U, Kenis P, Wagner D (2003) Communicating centrality in policy network drawings. IEEE Trans Vis Comput Graph 9(2):241–253

    Article  Google Scholar 

  5. Cao N, Sun J, Lin Y-R, Gotz D, Liu S, Qu H (2010) Facetatlas: multifaceted visualization for rich text corpora. IEEE Trans Vis Comput Graph 16(6):1172–1181

    Article  Google Scholar 

  6. Cao N, Gotz D, Sun J, Lin Y-R, Qu H (2011) Solarmap: multifaceted visual analytics for topic exploration. In: IEEE 11th international conference on data mining. IEEE, pp 101–110

  7. Chen C, Ibekwe-SanJuan F, San Juan E, Weaver C (2006) Visual analysis of conflicting opinions. In: IEEE symposium on visual analytics science and technology. IEEE, pp 59–66

  8. Eades P, Huang ML (2000) Navigating clustered graphs using force-directed methods. J Graph Algoritm Appl 4(3):157–181

    Article  MATH  Google Scholar 

  9. Farrugia M, Hurley N, Quigley A (2011) Exploring temporal ego networks using small multiples and tree-ring layouts. In: ACHI 2011, The Fourth International Conference on Advances in Computer-Human Interactions, pp 79–88

  10. Freeman LC (2000) Visualizing social networks. J Soc Struct 1(1):4

    Google Scholar 

  11. Gansner ER, Koren Y (2007) Improved circular layouts. In: Graph drawing. Springer, pp 386–398

  12. Gao J, Zhang C, Ke W, Ba S (2012) Understanding online purchase decision making: the effects of unconscious thought, information quality, and information quantity. Decis Support Syst 53(4):772–781

    Article  Google Scholar 

  13. Gilks WR, Richardson S, Spiegelhalter DJ (1996) Markov chain Monte Carlo in practice, vol 2. CRC press

  14. Gregory ML, Chinchor N, Whitney P, Carter R, Hetzler E, Alan Turner (2006) User-directed sentiment analysis: visualizing the affective content of documents. In: Proceedings of the Workshop on Sentiment and Subjectivity in Text. Association for Computational Linguistics, pp 23–30

  15. Heer J, Boyd D (2005) Vizster: Visualizing online social networks. In: IEEE symposium on information visualization. IEEE, pp 32–39

  16. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 168– 177

  17. Hu M, Liu B (2004) Mining opinion features in customer reviews. In: AAAI, vol. 4. pp 755–760

  18. Jensen D, Neville J, Gallagher B (2004) Why collective inference improves relational classification. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 593–598

  19. Kim K, Lee J (2014) Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction. Pattern Recogn 47(2):758–768

    Article  Google Scholar 

  20. Kim S-M, Hovy E (2004) Determining the sentiment of opinions. In: Proceedings of the 20th international conference on Computational Linguistics. Association for Computational Linguistics, pp 1367

  21. Koren Y, Carmel L, Harel D (2002) Ace: A fast multiscale eigenvectors computation for drawing huge graphs. In: IEEE symposium on information visualization, 2002.. IEEE, pp 137–144

  22. Li Y-M, Li T-Y (2013) Deriving market intelligence from microblogs. Decis Support Syst 55(1):206–217

    Article  Google Scholar 

  23. Li C, Wang F, Qi L, Liang F (2014) Experiment on sentiment embedded comparison interface. Knowl-Based Syst 64:44–58

    Article  Google Scholar 

  24. Liu B (2009) Opinion mining. Encyclopedia of Database Systems:1986–1990

  25. Liu B (2010) Sentiment analysis: a multi-faceted problem. IEEE Intell Syst 25(3):76–80

    Google Scholar 

  26. Liu B (2012) Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1):1–167

    Article  Google Scholar 

  27. Liu B, Hu M, Cheng J (2005) Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international conference on World Wide Web, pp 342–351. ACM

  28. Loia V, Senatore S (2014) A fuzzy-oriented sentic analysis to capture the human emotion in web-based content. Knowl-Based Syst 58(0):75–85

    Article  Google Scholar 

  29. Macskassy SA, Foster Provost (2003) A simple relational classifier. Technical report, DTIC Document

  30. Macskassy SA (2007) Improving learning in networked data by combining explicit and mined links. In: Proceedings of the national conference on artificial intellengence, vol 22. pp 590–595

  31. Macskassy SA, Provost F (2007) Classification in networked data: a toolkit and a univariate case study. J Mach Learn Res 8:935–983

    Google Scholar 

  32. McCallum AK, Nigam K, Rennie J, Seymore K (2000) Automating the construction of internet portals with machine learning. Inf Retr 3(2):127–163

    Article  Google Scholar 

  33. McDowell LK, Gupta KM, Aha DW (2009) Cautious collective classification. J Mach Learn Res 10:2777–2836

    MathSciNet  MATH  Google Scholar 

  34. Meng X, Wei F, Liu X, Zhou M, Li S, Wang H (2012) Entity-centric topic-oriented opinion summarization in twitter. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 379–387

  35. Montoyo A, Martínez-Barco P, Balahur A (2012) Subjectivity and sentiment analysis: an overview of the current state of the area and envisaged developments. Decis Support Syst 53(4):675–679

    Article  Google Scholar 

  36. Morinaga S, Yamanishi K, Tateishi K, Fukushima T (2002) Mining product reputations on the web. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 341–349. ACM

  37. Northway ML (1940) A method for depicting social relationships obtained by sociometric testing. Sociometry:144–150

  38. Neville J, Jensen D (2007) Relational dependency networks. J Mach Learn Res 8:653–692

    MATH  Google Scholar 

  39. Oelke D, Hao M, Rohrdantz C, Keim DA, Dayal U, Haug L-E, Janetzko H (2009) Visual opinion analysis of customer feedback data. In: IEEE symposium on visual analytics science and technology. IEEE, pp 187–194

  40. Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: LREC, vol 10. pp 1320–1326

  41. Perer A, Shneiderman B (2006) Balancing systematic and flexible exploration of social networks. IEEE Trans Vis Comput Graph 12(5):693–700

    Article  Google Scholar 

  42. Petz G, Karpowicz M, Fürschuß H, Auinger A, Winkler SM, Schaller S, Holzinger A (2012) On text preprocessing for opinion mining outside of laboratory environments. In: Active media technology, pages 618–629. Springer

  43. Petz G, Karpowicz M, Fürschuß H, Auinger A, Stříteskỳ V, Holzinger A (2013) Opinion mining on the web 2.0–characteristics of user generated content and their impacts. In: Human-computer interaction and knowledge discovery in complex, unstructured, big data. Springer, pp 35– 46

  44. Petz G, Karpowicz M, Fürschuß H, Auinger A, Stṙíteskỳ V, Holzinger A (2014) Computational approaches for mining users opinions on the web 2.0. Inf Process Manag 50(6):899–908

    Article  Google Scholar 

  45. Popescu A-M, Etzioni O (2007) Extracting product features and opinions from reviews. In: Natural language processing and text mining. Springer, pp 9–28

  46. Qing L, Getoor L (2003) Link-based classification. In: ICML, vol. 3, pp 496–503

  47. Sen P, Namata G, Bilgic M, Getoor L (2010) Collective classification. In: Encyclopedia of machine learning. Springer, pp 189–193

  48. Shen Z, Ma K-L, Eliassi-Rad T (2006) Visual analysis of large heterogeneous social networks by semantic and structural abstraction. IEEE Trans Vis Comput Graph 12(6):1427–1439

    Article  Google Scholar 

  49. Taskar B, Abbeel P, Koller D (2002) Discriminative probabilistic models for relational data. In: Proceedings of the eighteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., pp 485–492

  50. Therón R (2006) Hierarchical-temporal data visualization using a tree-ring metaphor. In: Smart graphics. Springer, pp 70–81

  51. Valdez AC, Schaar AK, Ziefle M, Holzinger A, Jeschke S, Brecher C (2012) Using mixed node publication network graphs for analyzing success in interdisciplinary teams. In: Active media technology. Springer, pp 606–617

  52. Yee K-P, Fisher D, Dhamija R, Hearst M (2001) Animated exploration of dynamic graphs with radial layout. In: Presented at IEEE symposium on information visualization

  53. Yingcai W, Wei F, Liu S, Norman A, Cui W, Zhou H, Huamin Q (2010) Opinionseer: interactive visualization of hotel customer feedback. IEEE Trans Vis Comput Graph 16(6):1109–1118

    Article  Google Scholar 

Download references

Acknowledgments

Yunming Ye’s work was supported in part by NSFC under Grant No. 61272538, Shenzhen Science and Technology Program under Grant No. JCYJ20140417172417128, and the Shenzhen Strategic Emerging Industries Program under Grant No. JCYJ20130329142551746. Raymond Y.K. Lau’s work was supported in part by Research Grants Council of the Hong Kong Special Administrative Region China under Grant No. CityU 145712, and the Shenzhen Municipal Science and Technology R&D Fund-Basic Research Program (JCYJ20130401145617281 and JCYJ20140419115614350). Yueping Li’s work was supported in part by NSFC under Grant No. 61303103, and the Shenzhen Science and Technology Program under Grant No. JCY20130331150354073.

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Authors and Affiliations

  1. Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

    Xiaolin Du, Yunming Ye & Xiaohui Huang

  2. Department of Information Systems, City University of Hong Kong, Hong Kong, SAR, China

    Raymond Y. K. Lau

  3. Shenzhen Polytechnic, Shenzhen, China

    Yueping Li

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  1. Xiaolin Du
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  2. Yunming Ye
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  5. Xiaohui Huang
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Correspondence to Yunming Ye.

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Du, X., Ye, Y., Lau, R.Y.K. et al. Multi-opinion Ring: visualizing and predicting multiple opinion orientations in online social media. Multimed Tools Appl 75, 7159–7186 (2016). https://doi.org/10.1007/s11042-015-2640-3

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