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Related Experiment Video

Updated: Aug 26, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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IDEST: International Database of Emotional Short Texts.

Johanna K Kaakinen1,2, Egon Werlen3, Yvonne Kammerer4,5

  • 1Department of Psychology and Speech-Language Pathology, University of Turku, Turku, Finland.

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Summary

We created the IDEST database of 250 short stories, rated for emotional valence and arousal across six languages. This resource aids cross-linguistic emotion research.

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Area of Science:

  • Psychology
  • Linguistics
  • Computational Linguistics

Background:

  • Emotional content analysis requires standardized stimuli.
  • Cross-linguistic validation of emotional ratings is crucial for global research.
  • Existing databases often lack multilingual emotional data.

Purpose of the Study:

  • To introduce the IDEST database, a collection of 250 short stories rated for valence, arousal, and comprehensibility.
  • To provide a multilingual resource for studying emotional responses to narratives.
  • To facilitate cross-linguistic comparisons of emotional perception in text.

Main Methods:

  • Collected ratings for valence, arousal, and comprehensibility for 250 short stories in six original languages and English translations.
  • Utilized an internet survey with 573 participants for English translation ratings.
  • Calculated readability indexes for all texts and categorized stories by emotional arc.

Main Results:

  • The IDEST database features texts with high comprehensibility and a wide range of emotional valence and arousal.
  • Valence ratings showed high cross-linguistic similarity; arousal and comprehensibility correlations were modest.
  • Comprehensibility ratings showed limited correlation with standard readability indexes.

Conclusions:

  • The IDEST database offers a valuable, freely available resource for emotion and narrative research.
  • Cross-linguistic emotional perception is largely consistent for valence but varies for arousal and comprehensibility.
  • Readability indexes partially predict text comprehensibility, highlighting the complexity of text perception.