Fuzzy-Set Based Sentiment Analysis of Big Social Data

Proceedings . IEEE 18th international Enterprise Distributed object computing conference

Volumn 2014-December, Issue December, 2014, Pages 71-80

  Mukkamala, Raghava Rao ^a   Hussain, Abid ^b   Vatrapu, Ravi ^b,c  

^a IT UNIVERSITY OF COPENHAGEN   (Denmark)

^b Department of Innovation and Organizational Economics   (Denmark)

^c Norwegian School of Information Technology (NITH)   (Norway)

Author keywords

Big Social Data; Computational Social Science; Data Science; Formal Methods; Social Data Analytics

Indexed keywords

COMPUTATION THEORY; DATA ANALYTICS; DATA SCIENCE; FORMAL METHODS; FUZZY SETS; GRAPH THEORY; PHILOSOPHICAL ASPECTS; SEMANTICS; SENTIMENT ANALYSIS; SOCIAL NETWORKING (ONLINE); SOCIAL SCIENCES COMPUTING; SOCIOLOGY;

COMPUTATIONAL APPROACH; COMPUTATIONAL SOCIAL SCIENCE; GRAPH THEORETICAL APPROACH; OPERATIONAL SEMANTICS; PHILOSOPHICAL APPROACHES; SENTIMENT CLASSIFICATION; SOCIAL DATUM; SOCIAL MEDIA ANALYTICS;

FUZZY SET THEORY;

EID: 84937416731     PISSN: 15417719     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EDOC.2014.19     Document Type: Conference Paper

References (48)

1. R. Vatrapu, "Understanding social business." in Emerging Dimensions of Technology Management, K. B. Akhilesh, Ed. Springer, 2013, pp. 147-158.
2. S. P. Robertson, R. K. Vatrapu, and R. Medina, "Off the wall political discourse: Facebook use in the 2008 u.s. presidential election, " Info. Pol., vol. 15, no. 1, 2, pp. 11-31, Apr. 2010.
3. S. Robertson, R. K. Vatrapu, and R. Medina, "Online video friends social networking: Overlapping online public spheres in the 2008 u.s. presidential election, " Journal of Information Technology & Politics, vol. 7, no. 2-3, pp. 182-201, 2010.
4. S. Asur and B. Huberman, "Predicting the future with social media, " in International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010, vol. 1, 2010, pp. 492-499.
5. N. Lassen, R. Madsen, and R. Vatrapu, "Predicting iphone sales from iphone tweets, " in Proceedings of the 18th IEEE Enterprise Computing Conference (EDOC 2014), 2014.
6. S. Bankes, R. Lempert, and S. Popper, "Making computational social science effective: Epistemology, methodology, and technology, " Soc. Sci. Comput. Rev., vol. 20, no. 4, pp. 377-388, Oct. 2002. [Online]. Available: http://dx.doi.org/10.1177/089443902237317
7. R. Conte, N. Gilbert, G. Bonelli, C. Cioffi-Revilla, G. Deffuant, J. Kertesz, V. Loreto, S. Moat, J.-P. Nadal, A. Sanchez, A. Nowak, A. Flache, M. San Miguel, and D. Helbing, "Manifesto of computational social science, " The European Physical Journal Special Topics, vol. 214, no. 1, 2012.
8. R. K. Vatrapu, "Explaining culture: An outline of a theory of sociotechnical interactions, " in Proceedings of the 3rd International Conference on Intercultural Collaboration, ser. ICIC '10. New York, NY, USA: ACM, 2010, pp. 111-120.
9. R. R. Mukkamala, A. Hussain, and R. Vatrapu, "Towards a formal model of social data, " IT University of Copenhagen, Denmark, IT University Technical Report Series TR-2013-169, November 2013.
10. A. Hussain and R. Vatrapu, "Social data analytics tool (sodato), " in DESRIST-2014 Conference (in press), ser. Lecture Notes in Computer Science (LNCS). Springer, 2014.
11. R. R. Mukkamala, A. Hussain, and R. Vatrapu, "Towards a set theoretical approach to big data analytics, " in 3rd International Congress on Big Data (IEEE BigData 2014), June 2014.
12. H. Li and J. D. Leckenby, Internet Advertising: Theory and Research. Psychology Press; 2 edition, 2007, ch. Internet Advertising Formats and Effectiveness.
13. C. P. Haugtvedt, P. M. Herr, and F. R. Kardes, Handbook of consumer psychology. Psychology Press, 2012.
14. C. Vollmer and G. Precourt, Always On: Advertising, Marketing, and Media in an Era of Consumer Contro. McGraw Hill Professional, 2008.
15. K. Kunst and R. Vatrapu, "Towards a theory of socially shared consumption: Literature review, taxonomy and research agenda, " in Proceedings of the European Conference on Information Systems (ECIS), Tel Aviv, Israel, 2014.
16. T. E. Barry, "The development of the hierarchy of effects: An historical perspective, " Current issues and Research in Advertising, vol. 10, no. 1-2, pp. 251-295, 1987.
17. R. J. Lavidge and G. A. Steiner, "A model for predictive measurements of advertising effectiveness." Journal of marketing, vol. 25, 1961.
18. T. Veblen, The theory of the leisure class; an economic study of institutions. Oxford University Press, 1899(2009).
19. A. Nowak, A. Rychwalska, and W. Borkowski, "Why simulate to develop a mental model, " Journal of Artificial Societies and Social Simulation, vol. 16, no. 3, 2013.
20. A. Hall, "Realising the benefits of formal methods, " in Formal Methods and Software Engineering, ser. LNCS. Springer Berlin Heidelberg, 2005, vol. 3785.
21. M. Emirbayer, "Manifesto for a relational sociology, " The American Journal of Sociology, vol. 103(2), pp. 281-317, 1997.
22. S. P. Borgatti, A. Mehra, D. J. Brass, and G. Labianca, "Network analysis in the social sciences, " Science, vol. 323(5916), pp. 892-895, 2009.
23. S. Wasserman and K. Faust, Social network analysis: Methods and applications (Vol. 8). Cambridge university press, 1994.
24. B. Pang and L. Lee, "Opinion mining and sentiment analysis, " Foundations and trends in information retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.
25. D. Hardt and J. Wulff, "What is the meaning of 5∗'s An investigation of the expression and rating of sentiment, " in Proceedings of KONVENS 2012, J. Jancsary, Ed. OGAI, September 2012, pp. 319-326, pATHOS 2012 workshop.
26. L. Zadeh, "Fuzzy sets, " Information and Control, vol. 8, no. 3, pp. 338-353, 1965.
27. H.-J. Zimmermann, "Fuzzy set theory, " Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 3, pp. 317-332, 2010. [Online]. Available: http://dx.doi.org/10.1002/wics.82
28. V. Dimitrov, "Social choice and self-organization under fuzzy management, " Kybernetes, vol. 6, no. 3, pp. 153-156, 1977.
29. -, "Use of fuzzy logic when dealing with social complexity, " Compexity International, vol. 04, January 1997.
30. C. C. Ragin, Fuzzy-set social science. University of Chicago Press, 2000.
31. N. M. Tichy, M. L. Tushman, and C. Fombrun, "Social network analysis for organizations, " The Academy of Management Review, vol. 4, no. 4, October 1979.
32. D. Krackhardt, "Cognitive social structures, " Social Networks, vol. 9, no. 2, pp. 109-134, Jun. 1987.
33. J. Zhan and X. Fang, "Social computing: the state of the art, " International Journal of Social Computing and Cyber-Physical Systems, vol. 1, no. 1, pp. 1-12, 2011.
34. J. Sabater and C. Sierra, "Reputation and social network analysis in multi-agent systems, " in Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 1, ser. AAMAS '02. New York, NY, USA: ACM, 2002, pp. 475-482.
35. J. Karikoski and M. Nelimarkka, "Measuring social relations with multiple datasets, " IJSCCPS, vol. 1, no. 1, pp. 98-113, 2011.
36. M. Goldberg, S. Kelley, M. Magdon-Ismail, K. Mertsalov, and A. Wallace, "Finding overlapping communities in social networks, " in IEEE Conference on Social Computing (SocialCom), 2010, pp. 104-113.
37. O. Macindoe and W. Richards, "Comparing networks using their fine structure, " International Journal of Social Computing and Cyber-Physical Systems, vol. 1, no. 1, 2011.
38. C. R. Fink, D. S. Chou, J. J. Kopecky, and A. J. Llorens, "Coarse-and fine-grained sentiment analysis of social media text, " Johns Hopkins APL Technical Digest, vol. 30, no. 1, pp. 22-30, 2011.
39. M. Grassi, E. Cambria, A. Hussain, and F. Piazza, "Sentic web: A new paradigm for managing social media affective information, " Cognitive Computation, vol. 3, no. 3, pp. 480-489, 2011.
40. T. Nguyen, D. Phung, B. Adams, and S. Venkatesh, "Prediction of age, sentiment, and connectivity from social media text, " in Web Information System Engineering-WISE 2011. Springer, 2011, pp. 227-240.
41. M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, "Lexiconbased methods for sentiment analysis, " Computational linguistics, vol. 37, no. 2, pp. 267-307, 2011.
42. T. Wilson, J. Wiebe, and P. Hoffmann, "Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis, " Computational linguistics, vol. 35, no. 3, pp. 399-433, 2009.
43. H. Chen, P. De, Y. Hu, and B.-H. Hwang, "Sentiment revealed in social media and its effect on the stock market, " in Statistical Signal Processing Workshop (SSP), 2011 IEEE. IEEE, 2011, pp. 25-28.
44. S. P. Robertson, "Changes in referents and emotions over time in election-related social networking dialog, " in System Sciences (HICSS), 2011 44th Hawaii International Conference on, 2011, pp. 1-9.
45. M. Salathé and S. Khandelwal, "Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control, " PLoS computational biology, vol. 7, no. 10, 2011.
46. T. Menezes, C. Roth, and J.-P. Cointet, "Finding the semantic-level precursors on a blog network." IJSCCPS, vol. 1, pp. 115-134, 2011.
47. Google-Inc, "Google prediction api, " September 2012, https://developers.google.com/prediction/.
48. B. Latour, Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford University Press, USA, 2005.