IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i13p2253-d849035.html
   My bibliography  Save this article

Predicting Change in Emotion through Ordinal Patterns and Simple Symbolic Expressions

Author

Listed:
  • Yair Neuman

    (The Functor Lab, Department of Cognitive and Brain Science, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel)

  • Yochai Cohen

    (The Functor Lab, Department of Cognitive and Brain Science, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel)

Abstract

Human interlocutors may use emotions as an important signaling device for coordinating an interaction. In this context, predicting a significant change in a speaker’s emotion may be important for regulating the interaction. Given the nonlinear and noisy nature of human conversations and relatively short time series they produce, such a predictive model is an open challenge, both for modeling human behavior and in engineering artificial intelligence systems for predicting change. In this paper, we present simple and theoretically grounded models for predicting the direction of change in emotion during conversation. We tested our approach on textual data from several massive conversations corpora and two different cultures: Chinese (Mandarin) and American (English). The results converge in suggesting that change in emotion may be successfully predicted, even with regard to very short, nonlinear, and noisy interactions.

Suggested Citation

  • Yair Neuman & Yochai Cohen, 2022. "Predicting Change in Emotion through Ordinal Patterns and Simple Symbolic Expressions," Mathematics, MDPI, vol. 10(13), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2253-:d:849035
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/13/2253/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/13/2253/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christoph Bandt, 2020. "Order patterns, their variation and change points in financial time series and Brownian motion," Statistical Papers, Springer, vol. 61(4), pages 1565-1588, August.
    2. Makridakis, Spyros & Taleb, Nassim, 2009. "Living in a world of low levels of predictability," International Journal of Forecasting, Elsevier, vol. 25(4), pages 840-844, October.
    3. Borges, João B. & Ramos, Heitor S. & Mini, Raquel A.F. & Rosso, Osvaldo A. & Frery, Alejandro C. & Loureiro, Antonio A.F., 2019. "Learning and distinguishing time series dynamics via ordinal patterns transition graphs," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
    4. Katsikopoulos, Konstantinos V. & Durbach, Ian N. & Stewart, Theodor J., 2018. "When should we use simple decision models? A synthesis of various research strands," Omega, Elsevier, vol. 81(C), pages 17-25.
    Full references (including those not matched with items on IDEAS)

    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. Betken, Annika & Dehling, Herold & Nüßgen, Ines & Schnurr, Alexander, 2021. "Ordinal pattern dependence as a multivariate dependence measure," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    2. Bent Flyvbjerg & Alexander Budzier & Daniel Lunn, 2021. "Regression to the tail: Why the Olympics blow up," Environment and Planning A, , vol. 53(2), pages 233-260, March.
    3. López Menéndez, Ana Jesús & Pérez Suárez, Rigoberto, 2017. "Forecasting Performance and Information Measures. Revisiting the M-Competition /Evaluación de Predicciones y Medidas de Información. Reexamen de la M-Competición," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 299-314, Mayo.
    4. Baolong Ying & Qijing Yan & Zehua Chen & Jinchao Du, 2024. "A sequential feature selection approach to change point detection in mean-shift change point models," Statistical Papers, Springer, vol. 65(6), pages 3893-3915, August.
    5. Bouckaert, Nicolas & Van den Heede, Koen & Van de Voorde, Carine, 2018. "Improving the forecasting of hospital services: A comparison between projections and actual utilization of hospital services," Health Policy, Elsevier, vol. 122(7), pages 728-736.
    6. Kuller, M. & Beutler, P. & Lienert, J., 2023. "Preference change in stakeholder group-decision processes in the public sector: Extent, causes and implications," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1268-1285.
    7. Sonntag, Dominik, 2018. "Die Theorie der fairen geometrischen Rendite [The Theory of Fair Geometric Returns]," MPRA Paper 87082, University Library of Munich, Germany.
    8. Roberto Savona & Marika Vezzoli, 2015. "Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 66-92, February.
    9. Cinelli, Marco & Kadziński, Miłosz & Gonzalez, Michael & Słowiński, Roman, 2020. "How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy," Omega, Elsevier, vol. 96(C).
    10. Miller, Craig & Newell, Barry, 2013. "Framing integrated research to address a dynamically complex issue: The red headed cockchafer challenge," Agricultural Systems, Elsevier, vol. 117(C), pages 13-18.
    11. Dowling, Natalie A. & Dichmont, Catherine M. & Leigh, George M. & Pascoe, Sean & Pears, Rachel J. & Roberts, Tom & Breen, Sian & Cannard, Toni & Mamula, Aaron & Mangel, Marc, 2020. "Optimising harvest strategies over multiple objectives and stakeholder preferences," Ecological Modelling, Elsevier, vol. 435(C).
    12. Soria-Lara, Julio A. & Ariza-Álvarez, Amor & Aguilera-Benavente, Francisco & Cascajo, Rocío & Arce-Ruiz, Rosa M. & López, Cristina & Gómez-Delgado, Montserrat, 2021. "Participatory visioning for building disruptive future scenarios for transport and land use planning," Journal of Transport Geography, Elsevier, vol. 90(C).
    13. Zanoli, Raffaele & Gambelli, Danilo & Vairo, Daniela, 2012. "Scenarios of the organic food market in Europe," Food Policy, Elsevier, vol. 37(1), pages 41-57.
    14. Amadio, Ariel & Rey, Andrea & Legnani, Walter & Blesa, Manuel García & Bonini, Cristian & Otero, Dino, 2023. "Mathematical and informational tools for classifying blood glucose signals - a pilot study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    15. Katsikopoulos, Konstantinos V. & Şimşek, Özgür & Buckmann, Marcus & Gigerenzer, Gerd, 2022. "Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?," International Journal of Forecasting, Elsevier, vol. 38(2), pages 613-619.
    16. Katsikopoulos, Konstantinos V. & Egozcue, Martin & Garcia, Luis Fuentes, 2022. "A simple model for mixing intuition and analysis," European Journal of Operational Research, Elsevier, vol. 303(2), pages 779-789.
    17. Maria Reznakova & Michal Karas, 2012. "The Effects Of A Change In The Environment On Business Valuation Using The Income Capitalization Approach," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 7(2), pages 119-137, June.
    18. Grossmann, Igor & Rotella, Amanda A. & Hutcherson, Cendri & Sharpinskyi, Konstantyn & Varnum, Michael E. W. & Achter, Sebastian K. & Dhami, Mandeep & Guo, Xinqi Evie & Kara-Yakoubian, Mane R. & Mandel, 2023. "Insights into the accuracy of social scientists' forecasts of societal change," Other publications TiSEM c14f4a4a-b105-46b3-90f7-f, Tilburg University, School of Economics and Management.
    19. Amaury Caruzzo & Cintia Maria Rodrigues Blanco & Paul Joe, 2020. "Developing a multi-attribute decision aid model for selection of a weather radar supplier," Environment Systems and Decisions, Springer, vol. 40(3), pages 371-384, September.
    20. Wang, Xiaoyan & Tang, Ming & Guan, Shuguang & Zou, Yong, 2023. "Quantifying time series complexity by multi-scale transition network approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).

    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:gam:jmathe:v:10:y:2022:i:13:p:2253-:d:849035. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.