Development and Validation of the Emotion Laden Sentences Toolbox for Emotion Recognition (ELSTER)

Abstract

Introduction: Emotion recognition plays a crucial role in our social interactions and overall well-being. The present cross-sectional study aimed to develop and validate Emotion Laden Sentences Toolbox for Emotion Recognition (ELSTER), that utilizes emotion-laden sentences as stimuli to assess individuals’ ability to perceive and identify emotions conveyed through written language. Methods: In Phase I, a comprehensive set of emotion-laden sentences in English language were validated by 25 (eight males and 17 females) qualified mental health professionals (MHPs). In Phase II, the sentences that received high interrater agreement in Phase I were selected and then a Hindi version of the same sentences was also developed. The English and Hindi database was then validated among 50 healthy individuals (30 males and 20 females). Results: The percentage hit rate for all the emotions after exclusion of contempt was 84.3% with a mean kappa for emotional expression being 0.67 among MHPs. The percentage hit rate of all emotion-laden sentences across the database was 81.43% among healthy lay individuals. The mean hit rate percentage for English sentences was similar to Hindi sentences with a mean kappa for emotional expression being 0.63 for the combined English and Hindi sentences. Conclusion: The ELSTER database would be useful in the Indian context for researching textual emotion recognition. It has been validated among a group of experts as well as healthy lay individuals and was found to have high inter-rater reliability.