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Mobile Information Systems
Volume 2015, Article ID 745095, 20 pages
http://dx.doi.org/10.1155/2015/745095
Research Article

A Sentiment Delivering Estimate Scheme Based on Trust Chain in Mobile Social Network

1College of Electronics and Information Engineering, Tongji University, Shanghai 200092, China
2College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China

Received 5 July 2015; Revised 20 September 2015; Accepted 27 September 2015

Academic Editor: Jose Juan Pazos-Arias

Copyright © 2015 Meizi Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Groenewegen and C. Moser, “Online communities: challenges and opportunities for mobile social network research,” Research in the Sociology of Organizations, vol. 40, pp. 463–477, 2014. View at Google Scholar
  2. D. Boyd, S. Golder, and G. Lotan, “Tweet, tweet, retweet: conversational aspects of retweeting on twitter,” in Proceedings of the 43rd Annual Hawaii International Conference on System Sciences (HICSS '10), pp. 1–10, IEEE, Honolulu, Hawaii, USA, January 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. H. He, “Sentiment analysis of Sina Weibo based on semantic sentiment space model,” in Proceedings of the 20th International Conference on Management Science and Engineering (ICMSE '13), pp. 206–211, IEEE, Harbin, China, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. 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. View at Publisher · View at Google Scholar · View at Scopus
  5. M. M. Mostafa, “More than words: social networks' text mining for consumer brand sentiments,” Expert Systems with Applications, vol. 40, no. 10, pp. 4241–4251, 2013. View at Publisher · View at Google Scholar
  6. A. Jøsang, R. Ismail, and C. Boyd, “A survey of trust and reputation systems for online service provision,” Decision Support Systems, vol. 43, no. 2, pp. 618–644, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. E. W. K. See-To and K. K. W. Ho, “Value co-creation and purchase intention in mobile social network sites: the role of electronic Word-of-Mouth and trust—a theoretical analysis,” Computers in Human Behavior, vol. 31, pp. 182–189, 2014. View at Google Scholar
  8. J. Wu and F. Chiclana, “A social network analysis trust–consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations,” Knowledge-Based Systems, vol. 59, pp. 97–107, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Wang and X.-L. Gui, “Selecting and trust computing for transaction nodes in online social networks,” Jisuanji Xuebao, vol. 36, no. 2, pp. 368–383, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Li, Q. Lv, X. Xie et al., “Interest-based real-time content recommendation in online social communities,” Knowledge-Based Systems, vol. 28, pp. 1–12, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. Z. Peiyun, C. Enhong, and L. Bo, “Web services trust computation based on mobile social network dynamic feedback,” Pattern Recognition and Artificial Intelligence, vol. 26, no. 4, pp. 337–343, 2013. View at Google Scholar
  12. X.-Q. Qiao, C. Yang, X.-F. Li, and J.-L. Chen, “A trust calculating algorithm based on social networking service users' context,” Jisuanji Xuebao, vol. 34, no. 12, pp. 2403–2413, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Javier Ortega, J. A. Troyano, F. L. Cruz, C. G. Vallejo, and F. Enríquez, “Propagation of trust and distrust for the detection of trolls in a social network,” Computer Networks, vol. 56, no. 12, pp. 2884–2895, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. B. Qureshi, G. Min, and D. Kouvatsos, “Trusted information exchange in peer-to-peer mobile social networks,” Concurrency Computation: Practice and Experience, vol. 24, no. 17, pp. 2055–2068, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. H. Zhao and X. Li, “VectorTrust: trust vector aggregation scheme for trust management in peer-to-peer networks,” in Proceedings of the 18th International Conference on Computer Communications and Networks (ICCCN '09), pp. 1–6, IEEE, San Francisco, Calif, USA, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Golbeck, “Trust and nuanced profile similarity in online social networks,” ACM Transactions on the Web, vol. 3, no. 4, article 12, 2009. View at Publisher · View at Google Scholar
  17. N. Li and D. D. Wu, “Using text mining and sentiment analysis for online forums hotspot detection and forecast,” Decision Support Systems, vol. 48, no. 2, pp. 354–368, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Tan, X. Cheng, Y. Wang, and H. Xu, “Adapting naive bayes to domain adaptation for sentiment analysis,” in Advances in Information Retrieval, vol. 5478 of Lecture Notes in Computer Science, pp. 337–349, Springer, Berlin, Germany, 2009. View at Publisher · View at Google Scholar
  19. J. Boyd-Graber and P. Resnik, “Holistic sentiment analysis across languages: multilingual supervised latent Dirichlet allocation,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '10), pp. 45–55, Association for Computational Linguistics, October 2010. View at Scopus
  20. M. Thelwall and K. Buckley, “Topic-based sentiment analysis for the social web: the role of mood and issue-related words,” Journal of the American Society for Information Science and Technology, vol. 64, no. 8, pp. 1608–1617, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. E. Cambria, B. Schuller, B. Liu, H. Wang, and C. Havasi, “Knowledge-based approaches to concept-level sentiment analysis,” IEEE Intelligent Systems, vol. 28, no. 2, pp. 12–14, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. F. Greaves, D. Ramirez-Cano, C. Millett, A. Darzi, and L. Donaldson, “Use of sentiment analysis for capturing patient experience from free-text comments posted online,” Journal of Medical Internet Research, vol. 15, no. 11, article e239, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. E. Kontopoulos, C. Berberidis, T. Dergiades, and N. Bassiliades, “Ontology-based sentiment analysis of twitter posts,” Expert Systems with Applications, vol. 40, no. 10, pp. 4065–4074, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. T. Nasukawa and J. Yi, “Sentiment analysis: capturing favorability using natural language processing,” in Proceedings of the 2nd International Conference on Knowledge Capture (K-CAP '03), pp. 70–77, ACM, Sanibel Island, Fla, USA, October 2003. View at Publisher · View at Google Scholar
  25. P. Gonçalves, M. Araújo, F. Benevenuto, and M. Cha, “Comparing and combining sentiment analysis methods,” in Proceedings of the 1st ACM Conference on Online Social Networks (COSN '13), pp. 27–37, ACM, Boston, Mass, USA, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. Q. Su, X. Xu, H. Guo et al., “Hidden sentiment association in Chinese web opinion mining,” in Proceedings of the 17th International Conference on World Wide Web (WWW '08), pp. 959–968, ACM, Beijing, China, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Athar and S. Teufel, “Detection of implicit citations for sentiment detection,” in Proceedings of the Workshop on Detecting Structure in Scholarly Discourse (DSSD '12), pp. 18–26, Association for Computational Linguistics, July 2012.
  28. A. Balahur, J. M. Hermida, and A. Montoyo, “Detecting implicit expressions of sentiment in text based on commonsense knowledge,” in Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA '11), Association for Computational Linguistics, Portland, Ore, USA, June 2011.
  29. A. Balahur, J. M. Hermida, and A. Montoyo, “Detecting implicit expressions of emotion in text: a comparative analysis,” Decision Support Systems, vol. 53, no. 4, pp. 742–753, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. X. Zhou, X. Tao, J. Yong, and Z. Yang, “Sentiment analysis on tweets for social events,” in Proceedings of the IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD '13), pp. 557–562, Whistler, Canada, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. T. Dergiades, C. Milas, and T. Panagiotidis, “Tweets, Google trends, and sovereign spreads in the GIIPS,” Oxford Economic Papers, vol. 67, no. 2, pp. 406–432, 2015. View at Publisher · View at Google Scholar
  32. M. Thelwall, D. Wilkinson, and S. Uppal, “Data mining emotion in social network communication: gender differences in MySpace,” Journal of the American Society for Information Science and Technology, vol. 61, no. 1, pp. 190–199, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. T. H. Soliman, M. A. Elmasry, A. Hedar, and M. M. Doss, “Sentiment analysis of Arabic slang comments on facebook,” International Journal of Computers & Technology, vol. 12, no. 5, pp. 3470–3478, 2014. View at Google Scholar
  34. A. Severyn, A. Moschitti, O. Uryupina, B. Plank, and K. Filippova, “Multi-lingual opinion mining on YouTube,” Information Processing & Management, 2015. View at Publisher · View at Google Scholar
  35. C. Baecchi, T. Uricchio, M. Bertini, and A. Del Bimbo, “A multimodal feature learning approach for sentiment analysis of social network multimedia,” Multimedia Tools and Applications, 2015. View at Publisher · View at Google Scholar
  36. C. Clavel and Z. Callejas, “Sentiment analysis: from opinion mining to human-agent interaction,” IEEE Transactions on Affective Computing, 2016. View at Publisher · View at Google Scholar