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Taxonomy-Based Detection of User Emotions for Advanced Artificial Intelligent Applications

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Hybrid Artificial Intelligent Systems (HAIS 2018)

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Abstract

Catching the attention of a new acquaintance and empathize with her can improve the social skills of a robot. For this reason, we illustrate here the first step towards a system which can be used by a social robot in order to “break the ice” between a robot and a new acquaintance. After a training phase, the robot acquires a sub-symbolic coding of the main concepts being expressed in tweets about the IAB Tier-1 categories. Then this knowledge is used to catch the new acquaintance interests, which let arouse in her a joyful sentiment. The analysis process is done alongside a general small talk, and once the process is finished, the robot can propose to talk about something that catches the attention of the user, hopefully letting arise in him a mix of feelings which involve surprise and joy, triggering, therefore, an engagement between the user and the social robot.

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References

  1. Agostaro, F., Augello, A., Pilato, G., Vassallo, G., Gaglio, S.: A conversational agent based on a conceptual interpretation of a data driven semantic space. In: Bandini, S., Manzoni, S. (eds.) AI*IA 2005. LNCS (LNAI), vol. 3673, pp. 381–392. Springer, Heidelberg (2005). https://doi.org/10.1007/11558590_39

    Chapter  Google Scholar 

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

    MATH  Google Scholar 

  3. Brethes, L., Menezes, P., Lerasle, F., Hayet, J.: Face tracking and hand gesture recognition for human-robot interaction. In: IEEE International Conference on Robotics and Automation, vol 2, pp 1901–1906. IEEE (2004)

    Google Scholar 

  4. Chella, A., Frixione, M., Gaglio, S.: A cognitive architecture for robot self consciousness. Artif. Intell. Med. 44(2), 147–154 (2008)

    Article  Google Scholar 

  5. Cannataro, M., Cuzzocrea, A., Pugliese, A.: A probabilistic approach to model adaptive hypermedia systems. In: 1st International Workshop on Web Dynamics, in conjunction on ICDT 2001 (2001)

    Google Scholar 

  6. Corrigan, L.J., Peters, C., Küster, D., Castellano, G.: Engagement perception and generation for social robots and virtual agents. In: Esposito, A., Jain, L.C. (eds.) Toward Robotic Socially Believable Behaving Systems - Volume I. ISRL, vol. 105, pp. 29–51. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31056-5_4

    Chapter  Google Scholar 

  7. Cuzzocrea, A., Fortino, G., Rana, O.: Managing Data and processes in cloud-enabled large-scale sensor networks: state-of-the-art and future research directions. In: 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2013, pp. 583–588 (2013)

    Google Scholar 

  8. D’Avanzo, E., Pilato, G.: Mining social network users opinions’ to aid buyers’ shopping decisions. Comput. Hum. Behav. 51, 1284–1294 (2014)

    Article  Google Scholar 

  9. D’Avanzo E., Pilato G., Lytras M.D.: Using Twitter sentiment and emotions analysis of Google trends for decisions making. Program 51(3), 322–350 (2017)

    Article  Google Scholar 

  10. Darling, W.M.: A theoretical and practical implementation tutorial on topic modeling and gibbs sampling. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 642–647, 1 Dec 2011

    Google Scholar 

  11. Delaherche, E., Dumas, G., Nadel, J., Chetouani, M.: Automatic measure of imitation during social interaction: a behavioral and hyperscanning-EEG benchmark. Pattern Recognit. Lett. 66, 118–126 (2015)

    Article  Google Scholar 

  12. Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17, 124 (1971)

    Article  Google Scholar 

  13. Interactive Advertising Bureau (IAB) Contextual Taxonomy. http://www.iab.net/. Retrieved December 2017

  14. Kanagasabai, R., Veeramani, A., Ngan, L.D., Yap, G.E., Decraene, J., Nash, A.S.: Using semantic technologies to mine customer insights in telecom industry. In: International Semantic Web Conference (Industry Track) (2014)

    Google Scholar 

  15. Landauer, T.K., Dumais, S.T.: A solution to Plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol. Rev. 104(2), 211–223 (1990)

    Article  Google Scholar 

  16. Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25, 259–284 (1998)

    Article  Google Scholar 

  17. Liu, B.: Sentiment analysis and subjectivity. In: Indurkhya, N., Damerau, F.J. (eds.) Handbook of Natural Language Processing, pp. 627–665. CRC Press (2010)

    Google Scholar 

  18. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)

    Google Scholar 

  19. Petherbridge, N.: Artifical Intelligence Scripting Language. Rivescript.com

  20. Pilato, G., D’Avanzo, E.: Data-driven social mood analysis through the conceptualization of emotional fingerprints. Procedia Comput. Sci. 123, 360–365 (2018)

    Article  Google Scholar 

  21. Santilli, S., Nota, L., Pilato, G.: The use of latent semantic analysis in the positive psychology: a comparison with Twitter posts. In: 2017 IEEE 11th International Conference on Semantic Computing (ICSC), pp. 494–498. IEEE (2017)

    Google Scholar 

  22. Strapparava, C., Mihalcea, R.: Semeval-2007 task 14: affective text. In: Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 70–74. Association for Computational Linguistics (2007)

    Google Scholar 

  23. Strapparava, C., Mihalcea, R.: Learning to identify emotions in text. In: SAC 2008 Proceedings of the 2008 ACM Symposium on Applied Computing (2008)

    Google Scholar 

  24. Siddharth, G., Borkar, D., De Mello, C., Patil, S.: An E-Commerce Website based Chatbot. Proc. (IJCSIT) Int. J. Comput. Sci. Inf. Technol. 6(2), 1483–1485 (2015)

    Google Scholar 

  25. Teh, Y.W., Newman, D., Welling, M.: A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation. NIPS 6, 1378–1385 (2006)

    Google Scholar 

  26. Terrana, D., Augello, A., Pilato, G.: Facebook users relationships analysis based on sentiment classification. In: Proceedings of 2014 IEEE International Conference on Semantic Computing (ICSC), pp. 290–296 (2014)

    Google Scholar 

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Correspondence to Giovanni Pilato .

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Cuzzocrea, A., Pilato, G. (2018). Taxonomy-Based Detection of User Emotions for Advanced Artificial Intelligent Applications. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_48

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  • DOI: https://doi.org/10.1007/978-3-319-92639-1_48

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-92639-1

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