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Multi-agent System for Privacy Protection Through User Emotions in Social Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 722))

Abstract

In this research privacy and decision making in social networks are addressed through a multi-agent system, using a model of the temperament of users, taking into account their emotions through the messages they put on the social media and the visual information obtained from them. We use opinion mining from a social network and images from users to get the data and calculate a model of temperament based on the PAD model generated from images and in the polarity of the messages they write. For this reason we propose a method to calculate a temperament state based on the history of PAD values of the user and the history of text polarities. We use also a method that analyzes the sentiment expressed in a message and helps the user to make the decision of posting it or not.

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References

  1. Vanderhoven, E., Schellens, T., Valcke, M.: Educating teens about the risks on social network sites. An intervention study in secondary education. Comunicar XXII(43), 123 (2014)

    Article  Google Scholar 

  2. Lewis, C.C.: How adolescents approach decisions: changes over grades seven to twelve and policy implications. Child Dev. 52(2), 538 (1981). http://dx.doi.org/10.2307/1129-172

    Article  Google Scholar 

  3. Vanderhoven, E., Schellens, T., Vanderlinde, R., Valcke, M.: Developing educational materials about risks on social network sites: a design based research approach. Educ. Tech. Res. Dev. 64, 459–480 (2016)

    Article  Google Scholar 

  4. Christofides, E., Muise, A., Desmarais, S.: Disclosures on Facebook: the effect of having a bad experience on online behavior. J. Adolesc. Res. 27, 714–731 (2012)

    Article  Google Scholar 

  5. Mehrabian, A.: Outline of a general emotion-based theory of temperament. In: Strelau, J., Angleitner, A. (eds.) Explorations in Temperament: International Perspectives on Theory and Measurement, pp. 75–86. Plenum Press, New York (1991)

    Chapter  Google Scholar 

  6. Liu, B.: Sentiment Analysis and Opinion Mining, series Synthesis Lectures on Human Language Technologies, vol. 16. Morgan, San Mateo (2012)

    Google Scholar 

  7. Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)

    Article  Google Scholar 

  8. Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2016)

    Article  Google Scholar 

  9. Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceedings of the 19th National Conference on Artifical Intelligence, pp. 755–760 (2004)

    Google Scholar 

  10. Zhao, Y., Qin, B., Hu, S., Liu, T.: Generalizing syntactic structures for product attribute candidate extraction. In: Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 377–380 (2010)

    Google Scholar 

  11. Jakob, N., Gurevych, I.: Extracting opinion targets in a single and cross-domain setting with conditional random fields. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1035–1045 (2010)

    Google Scholar 

  12. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  13. Blair-Goldensohn, S., Neylon, T., Hannan, K., Reis, G.A., Mcdonald, R., Reynar, J.: Building a sentiment summarizer for local service reviews. In: Proceedings of Workshop NLP Information Explosion (2008)

    Google Scholar 

  14. Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 339–346 (2005)

    Google Scholar 

  15. Nasukawa, T., Yi, J.: Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of 2nd International Conference Knowledge Capture, pp. 70–77 (2003)

    Google Scholar 

  16. Li, F., Han, C., Huang, M., Zhu, X., Xia, Y.-J., Zhang, S., Yu, H.: Structure-aware review mining and summarization. In: Proceedings of 23rd International Conference on Computational Linguistics, pp. 653–661 (2010)

    Google Scholar 

  17. Zhao, W.X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: Proceedings of Conference Empirical Methods Natural Language Processing, pp. 56–65 (2010)

    Google Scholar 

  18. Seroussi, Y., Zukerman, I., Bohnert, F.: Collaborative inference of sentiments from texts. In: Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 195–206. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13470-8_19

    Chapter  Google Scholar 

  19. Tang, D., Qin, B., Liu, T.: Learning semantic representations of users and products for document level sentiment classification. In: Proceedings of 53rd Annual Meeting of the Association for Computational Linguistics (2015)

    Google Scholar 

  20. Tang, D., Qin, B., Liu, T., Yang, Y.: User modeling with neural network for review rating prediction. In: Proceedings of 24th International Joint Conference Artifical Intelligence, pp. 1340–1346 (2015)

    Google Scholar 

  21. Li, F., Liu, N., Jin, H., Zhao, K., Yang, Q., Zhu, X.: Incorporating reviewer and product information for review rating prediction. In: Proceedings of 22th International Joint Conference Artifical Intelligence, vol. 11, pp. 1820–1825 (2011)

    Google Scholar 

  22. Gao, W., Yoshinaga, N., Kaji, N., Kitsuregawa, M.: Modeling user leniency and product popularity for sentiment classification. In: Proceedings of IJCNLP, Nagoya, Japan (2013)

    Google Scholar 

  23. Diao, Q., Qiu, M., Wu, C.-Y., Smola, A.J., Jiang, J., Wang, C.: Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In: Proceedings of 20th ACM SIGKDD International Conference Knowledge Discovery and Data Mining, pp. 193–202 (2014)

    Google Scholar 

  24. Rincon, J.A., de la Prieta, F., Zanardini, D., Julian, V., Carrascosa, C.: Influencing over people with a social emotional model. In: International Conference on Practical Applications of Agents and Multiagent Systems (2016)

    Google Scholar 

  25. Xie, W., Kang, C.: See you, see me: teenagers self-disclosure and regret of posting on social network site. Comput. Hum. Behav. 52, 398–407 (2015)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the project TIN2014-55206-R of the Spanish government.

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Correspondence to G. Aguado .

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Aguado, G., Julian, V., Garcia-Fornes, A. (2017). Multi-agent System for Privacy Protection Through User Emotions in Social Networks. In: Bajo, J., et al. Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems. PAAMS 2017. Communications in Computer and Information Science, vol 722. Springer, Cham. https://doi.org/10.1007/978-3-319-60285-1_20

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  • DOI: https://doi.org/10.1007/978-3-319-60285-1_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60284-4

  • Online ISBN: 978-3-319-60285-1

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