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Analyzing the Repercussions of the Actions Based on the Emotional State in Social Networks

  • Guillem Aguado
  • Vicente Julian
  • Ana Garcia-Fornes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10767)

Abstract

The present work is a study of the detection of negative affective or emotional states that people have using social network sites (SNSs), and the effect that this negative state has on the repercussions of posted messages. We aim to discover in which grade an user having an affective state considered negative by an analyzer (Sentiment Analyzer and Stress Analyzer), can affect other users and generate bad repercussions, and to know whether its more suitable to predict a bad future situation using the different analyzers. We propose a method for creating a combined model of sentiment and stress and use it in our experimentation in order to discern if it is more suitable to predict future bad situations, and in what context. Additionally, we created a Multi-Agent System (MAS) that integrate the analyzers to protect or advice users, which uses the trained and tested system to predict and avoid future bad situations in social media, that could be triggered by the actions of an user that has an emotional state considered negative. We conduct this study as a way to help building future systems that prevent bad situations where an user that has a negative state creates a repercussion in the system. This can help avoid users to achieve a bad mood, or help avoid privacy issues, in the way that an user that has a negative state post information that he don’t really want to post.

Keywords

Agents Multi-Agent System Social Networks Sentiment Analysis Stress Stress Analysis Advice Privacy Users 

Notes

Acknowledgments

This work was supported by the project TIN2014-55206-R of the Spanish government. This work was supported by the project TIN2017-89156-R of the Spanish government.

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Vanderhoven, E., Schellens, T., Valcke, M.: Educating teens about the risks on social network sites. An intervention study in secondary education. Comunicar 22(43), 123–132 (2014)CrossRefGoogle Scholar
  3. 3.
    Christofides, E., Muise, A., Desmarais, S.: Risky disclosures on facebook: the effect of having a bad experience on online behavior. J. Adolesc. Res. 27, 714–731 (2012)CrossRefGoogle Scholar
  4. 4.
    Ciccarelli, M., Griffiths, M.D., Nigro, G., Cosenza, M.: Decision making, cognitive distortions and emotional distress: a comparison between pathological gamblers and healthy controls. J. Behav. Ther. Exp. Psychiatry 54, 204–210 (2017)CrossRefGoogle Scholar
  5. 5.
    George, J.M., Dane, E.: Affect, emotion, and decision making. Organ. Behav. Hum. Decis. Processes 136, 47–55 (2016)CrossRefGoogle Scholar
  6. 6.
    Thelwall, M.: TensiStrength: stress and relaxation magnitude detection for social media texts. Inf. Process. Manag. 53, 106–121 (2017)CrossRefGoogle Scholar
  7. 7.
    Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, vol. 16. Morgan, San Mateo (2012)Google Scholar
  8. 8.
    Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)CrossRefGoogle Scholar
  9. 9.
    Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2016)CrossRefGoogle Scholar
  10. 10.
    Hu, M., Liu, B.: Mining opinion features in customer reviews. In Proceedings of the 19th National Conference on Artificial Intelligence, pp. 755–760 (2004)Google Scholar
  11. 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. 12.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  13. 13.
    Seroussi, Y., Zukerman, I., Bohnert, F.: Collaborative inference of sentiments from texts. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 195–206. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13470-8_19CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    Villena-Roman, J., Lana-Serrano, S., Martinez-Camara, E., Gonzalez-Cristobal, J.C.: TASS - workshop on sentiment analysis at SEPLN. Procesam. Leng. Nat. 50 (2013)Google Scholar
  18. 18.
    Villena-Roman, J., Garcia-Morera, J., Lana-Serrano, S., Gonzalez-Cristobal, J.C.: TASS 2013 - a second step in reputation analysis in Spanish. Proces. Leng. Nat. 52, 37–44 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Departamento de Sistemas Informáticos y Computación (DSIC)Universitat Politècnica de ValènciaValenciaSpain

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