IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v18y2018i1d10.1007_s10660-017-9265-8.html
   My bibliography  Save this article

Multi-layer affective computing model based on emotional psychology

Author

Listed:
  • Qingyuan Zhou

    (Changzhou Administrative College)

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.

Suggested Citation

  • Qingyuan Zhou, 2018. "Multi-layer affective computing model based on emotional psychology," Electronic Commerce Research, Springer, vol. 18(1), pages 109-124, March.
  • Handle: RePEc:spr:elcore:v:18:y:2018:i:1:d:10.1007_s10660-017-9265-8
    DOI: 10.1007/s10660-017-9265-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-017-9265-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10660-017-9265-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Eugene F. Fama & Kenneth R. French, 2016. "Dissecting Anomalies with a Five-Factor Model," The Review of Financial Studies, Society for Financial Studies, vol. 29(1), pages 69-103.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qingyuan Zhou & Zongming Zhang & Yuancong Wang, 2020. "WIT120 data mining technology based on internet of things," Health Care Management Science, Springer, vol. 23(4), pages 680-688, December.
    2. Satish Kumar & Weng Marc Lim & Nitesh Pandey & J. Christopher Westland, 2021. "20 years of Electronic Commerce Research," Electronic Commerce Research, Springer, vol. 21(1), pages 1-40, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giovanni Calice & Levent Kutlu & Ming Zeng, 2021. "Understanding US firm efficiency and its asset pricing implications," Empirical Economics, Springer, vol. 60(2), pages 803-827, February.
    2. Ball, Ray & Gerakos, Joseph & Linnainmaa, Juhani T. & Nikolaev, Valeri, 2016. "Accruals, cash flows, and operating profitability in the cross section of stock returns," Journal of Financial Economics, Elsevier, vol. 121(1), pages 28-45.
    3. Chaderina, Maria & Weiss, Patrick & Zechner, Josef, 2022. "The maturity premium," Journal of Financial Economics, Elsevier, vol. 144(2), pages 670-694.
    4. Zhong, Angel, 2018. "Idiosyncratic volatility in the Australian equity market," Pacific-Basin Finance Journal, Elsevier, vol. 50(C), pages 105-125.
    5. Hsu, Junming & Yang, Tung-Hsiao & Tsai, Yi-Chi, 2021. "The long-run performance of cross-border acquirers: An analysis of synergy sources," Journal of Multinational Financial Management, Elsevier, vol. 60(C).
    6. Ali, Fahad & Ülkü, Numan, 2021. "Quest for a parsimonious factor model in the wake of quality-minus-junk, misvaluation and Fama-French-six factors," Finance Research Letters, Elsevier, vol. 41(C).
    7. Irina Bezhentseva Mateus & Cesario Mateus & Natasa Todorovic, 2019. "Benchmark-adjusted performance of US equity mutual funds and the issue of prospectus benchmarks," Journal of Asset Management, Palgrave Macmillan, vol. 20(1), pages 15-30, February.
    8. Ahmed, Shamim & Bu, Ziwen & Symeonidis, Lazaros & Tsvetanov, Daniel, 2023. "Which factor model? A systematic return covariation perspective," Journal of International Money and Finance, Elsevier, vol. 136(C).
    9. Lin, Qi, 2019. "Residual momentum and the cross-section of stock returns: Chinese evidence," Finance Research Letters, Elsevier, vol. 29(C), pages 206-215.
    10. Kobana Abukari & Isaac Otchere, 2020. "Dominance of hybrid contratum strategies over momentum and contrarian strategies: half a century of evidence," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(4), pages 471-505, December.
    11. Jonathan Fletcher, 2018. "An Examination of the Benefits of Factor Investing in U.K. Stock Returns," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(4), pages 154-170, April.
    12. Chen, Jiun-Lin & Glabadanidis, Paskalis & Sun, Mingwei, 2022. "The five-factor asset pricing model, short-term reversal, and ownership structure – the case of China," International Review of Financial Analysis, Elsevier, vol. 82(C).
    13. Wan-Ni Lai & Yi-Ting Chen & Edward W. Sun, 2021. "Comonotonicity and low volatility effect," Annals of Operations Research, Springer, vol. 299(1), pages 1057-1099, April.
    14. Long, Huaigang & Zhu, Yanjian & Chen, Lifang & Jiang, Yuexiang, 2019. "Tail risk and expected stock returns around the world," Pacific-Basin Finance Journal, Elsevier, vol. 56(C), pages 162-178.
    15. Carmine De Franco & Johann Nicolle & Huyên Pham, 2019. "Dealing with Drift Uncertainty: A Bayesian Learning Approach," Risks, MDPI, vol. 7(1), pages 1-18, January.
    16. Zareei, Abalfazl, 2021. "Cross-momentum: Tracking idiosyncratic shocks," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 177-199.
    17. Yu Wang & Haicheng Shu, 2019. "Evaluating the Performance of Factor Pricing Models for Different Stock Market Trends: Evidence from China," Working Papers 2019-10-10, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    18. Mamdouh Medhat & Maik Schmeling, 2022. "Short-term Momentum," The Review of Financial Studies, Society for Financial Studies, vol. 35(3), pages 1480-1526.
    19. Berggrun, Luis & Cardona, Emilio & Lizarzaburu, Edmundo, 2020. "Firm profitability and expected stock returns: Evidence from Latin America," Research in International Business and Finance, Elsevier, vol. 51(C).
    20. Alex Dontoh & Fayez A. Elayan & Joshua Ronen & Tavy Ronen, 2021. "Unfair “Fair Value” in Illiquid Markets: Information Spillover Effects in Times of Crisis," Management Science, INFORMS, vol. 67(8), pages 5163-5193, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:18:y:2018:i:1:d:10.1007_s10660-017-9265-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.