Skip to main content
SpringerLink
Log in

Multi-layer affective computing model based on emotional psychology

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

The factors and transforms of affective state were analyzed based on affective psychology theory. After that, a multi-layer affective decision model was proposed by establishing mapping relation among character, mood and motion. The model reflected the changes of mood and emotion spaces based on different characters. Experiment showed that human emotion characteristics accorded with theory and law, thus providing reference for modeling of human–computer interaction system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Ma, J., Choo, K.-K. R., Hsu, H., Jin, Q., Liu, W., Wang, K., et al. (2016). Perspectives on cyber science and technology for cyberization and cyber-enabled worlds. In Proceedings of 14th IEEE International Conference on Dependable Autonomic and Secure Computing (DASC 2016) (pp. 1–9), IEEE Computer Society Press. 8–12 Aug 2016.

  2. Fardous, J., Du, T., Choo, K.-K. R., & Huang, S. (2017). Investigating mobile social media users’ behaviors in tourism collaborative information seeking. In Proceedings of ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2017) (pp. 395–397). Oslo, Norway, 7–11 Mar.

  3. Jitendra, R., Dalmia, Anmol., Choo, K.-K. R., Sambit, B., & Sanjay, J. (2017). Revisiting semi-supervised learning for online deceptive review detection. IEEE Access, 5, 1319–1327.

    Article  Google Scholar 

  4. Zhaolan, Meng. (1984). The new expansions of emotion research. Psychological Science, 01, 40–45.

    Google Scholar 

  5. Damasio, Antonio. (2006). Descartes’ error: Emotion, reason, and the human brain. New York: Random House.

    Google Scholar 

  6. Minsky, M. (1988). Society of mind. New York: Simon and Schuster.

    Google Scholar 

  7. Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people and places. Stanford: CSLI Publications and Cambridge.

    Google Scholar 

  8. Picard, R. W. (1997). Affective computing (pp. 129–142). Cambridge: MIT Press.

    Google Scholar 

  9. Healey, J., Seger, J., & Picard, R. (1999). Quantifying driver stress: Developing a system for collecting and processing bio-metric signals in natural situations. Biomedical Sciences Instrumentation, 35, 193–198.

    Google Scholar 

  10. Min, H. S., Liu, D., & Wang, T.-M. (2012). Research on speech control model of intelligent robot dog. Computer Engineering, 38(01), 188–191. doi:10.3969/j.issn.1000-3428.2012.01.060.

    Google Scholar 

  11. Xiaolan, Fu. (2008). Affective computing benefits to harmonious electronic society. Bulletin of Chinese Academy of Sciences, 23(5), 453–457.

    Google Scholar 

  12. Sloman, A. (2003). The cognition and affect project: Architectures, architecture-schemas, and the new science of mind [R]. Technical Report of School of Computer Science, University of Birmingham.

  13. Wang, Z. (2000). Artificial psychology-a most accessible science research to human brain. Journal of University of Science and Technology Beijing, 22(5), 478–481.

    Google Scholar 

  14. Zheng, Y., & Wen, G. (1996). Agent oriented technology in multiple functional perceptive system. Journal of Software, 7(3), 163–167.

    Google Scholar 

  15. Song, Y.-X., & Jia, P.-F. (2004). A control architecture based on artificial emotion for anthropomorphic robot. Robot, 26(6), 491–495.

    Google Scholar 

  16. Wang, Z.-L. (2006). Artificial psychology and artificial emotion. CAAI Transactions on Intelligent Systems, 1(1), 38–43.

    Google Scholar 

  17. Zhou, Q. (2016). Cluster Comput, 19, 1275. doi:10.1007/s10586-016-0580-y.

    Article  Google Scholar 

  18. Liu, Y., Fu, Q., & Fu, X. (2009). Chinese Science Bulletin, 54, 4102. doi:10.1007/s11434-009-0632-2.

    Article  Google Scholar 

  19. Ye, L., Linmi, T., & Xiaolan, F. (2009). The analysis of PAD emotional state model based on emotion pictures. Journal of Image and Graphics, 14(5), 753–758. doi:10.11834/jig.20090501.

    Google Scholar 

  20. Xie, L., Wang, Z., Dongchun, R., et al. (2010). Research of driver emotion model under simplified traffic condition. Acta Automatica Sinica, 36(12), 1732–1743.

    Article  Google Scholar 

  21. Calvo, R. A., D’Mello, S., Gratch, J., & Kappas, A. (Eds.). (2014). The oxford handbook of affective computing. USA: Oxford University Press.

    Google Scholar 

  22. Ortony, A., Clore, G. L., & Collins, A. (1990). The cognitive structure of emotions (pp. 150–161). Cambridge: Cambridge University Press.

    Google Scholar 

  23. Elliott, C. (1994). Multi-media communication with emotion-driven “believable agents”. In AAAI Technical Report for the Spring Symposium on Believable Agents (pp. 16–20). Stanford University: AAAI.

  24. Kshirsagar, S. (2002). A multilayer personality model. In Proceedings of the 2nd International Symposium on Smart Graphics. ACM, pp. 107–115. doi: 10.1145/569005.569021.

  25. Guoliang, Y., Zhiliang, W., Guojiang, W., & Fengjun, C. (2006). Affective computing model based on emotional psychology. In L. Jiao, L. Wang, X. Gao, J. Liu, F. Wu (eds) Advances in natural computation, ICNC 2006, Lecture Notes in Computer Science, vol. 4221. Berlin: Springer. doi: 10.1007/11881070_37.

  26. Fama, E. F., & French, K. R. (2016). Dissecting anomalies with a five-factor model. Review of Financial Studies, 29(1), 69–103. doi:10.1093/rfs/hhv043.

    Article  Google Scholar 

  27. Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6(3–4), 169–200.

    Article  Google Scholar 

  28. Mehrabian, A. (1996). Current Psychology, 14, 261. doi:10.1007/BF02686918.

    Article  Google Scholar 

  29. Picard, R. W., & Healey, J. (1997). Personal Technologies, 1, 231. doi:10.1007/BF01682026.

    Article  Google Scholar 

  30. Vitalkar, Punam. M., Bendre, P. N., & Gulhane, S. M. (2016). Analysis of affective speech signals for emotion extraction and attitude prediction. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 5(7), 6243–6251. doi:10.15662/IJAREEIE.2016.0507068.

    Google Scholar 

  31. Liu, C. Z., Jozani, M. M., & Choo, K.-K. R. (2017). The effect of the recommendation system in the mobile app market. In Proceedings of 23rd Americas Conference on Information Systems (AMCIS 2017). Association for Information Systems, 10–12 Aug 2017.

  32. Rout, J. K., Choo, K. K. R., Dash, A. K., et al. (2017). Electronic Commerce Research. doi:10.1007/s10660-017-9257-8.

    Google Scholar 

  33. Ling-jie, T. (2007). Emotion designing products-on the emotional methodology in the R & D of products. Hundred Schools in Art, 6, 146–149.

    Google Scholar 

Download references

Acknowledgements

This work is supported by the Social Sciences Foundation of Jiangsu Province (No. 16EYD006).

Author information

Authors and Affiliations

  1. Department of Economics and Management, Changzhou Administrative College, Changzhou, China

    Qingyuan Zhou

Authors
  1. Qingyuan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingyuan Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Q. Multi-layer affective computing model based on emotional psychology. Electron Commer Res 18, 109–124 (2018). https://doi.org/10.1007/s10660-017-9265-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-017-9265-8

Keywords

Search

Navigation