Predicting Change in Emotion through Ordinal Patterns and Simple Symbolic Expressions

Yair Neuman and Yochai Cohen
Additional contact information
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

Mathematics, 2022, vol. 10, issue 13, 1-18

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.

Keywords: emotion dynamics; short-term prediction; ordinal patterns; symbolic regression/classification; simple models; processing; interdisciplinary research (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/13/2253/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/13/2253/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:13:p:2253-:d:849035

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().