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.
Background: Emotional facial expression database, used in emotion regulation studies, is a special set of pictures with high social and biological relevance. We present the AIIMS Facial Toolbox for Emotion Recognition (AFTER) database. It consists of pictures of 15 adult professional artists displaying seven facial expressions-neutral, happiness, anger, sadness, disgust, fear, and surprise. Methods: This cross-sectional study enrolled 15 volunteer students from a professional drama college in India (six males and nine females; mean age = 26.2 ± 1.93 years). They were instructed to pose with different emotional expressions in high and low intensity. A total of 240 pictures were captured in a brightly lit room against a common, light background. Each picture was validated independently by 19 mental health professionals and two professional teachers of dramatic art. Apart from recognition of emotional quality, ratings were done for each emotion on a 5-point Likert scale with respect to three dimensions-intensity, clarity, and genuineness. Results are discussed in terms of mean scores on all four parameters. Results: The percentage hit rate for all the emotions, after exclusion of contempt, was 84.3%, with the mean kappa for emotional expression being 0.68. Mean scores on intensity, clarity, and genuineness of the emotions depicted in the pictures were high. Conclusions: The database would be useful in the Indian context for researching facial emotion recognition. It has been validated among a group of experts and was found to have high inter-rater reliability.
