Predicting Change in Emotion through Ordinal Patterns and Simple Symbolic Expressions

Yair Neuman, Yochai Cohen

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish
Article number2253
Issue number13
StatePublished - 1 Jul 2022


  • emotion dynamics
  • interdisciplinary research
  • ordinal patterns
  • processing
  • short-term prediction
  • simple models
  • symbolic regression/classification

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Mathematics
  • Engineering (miscellaneous)


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