Developing and validating the self-transcendent emotion dictionary for text analysis

Area of Science:

• Psychology
• Computational Linguistics
• Digital Humanities

Background:

• Growing research interest in self-transcendent emotions (e.g., awe, gratitude, hope).
• Need for tools to analyze these positive emotions in large text datasets.
• Existing methods for text analysis lack specificity for nuanced emotional expression.

Purpose of the Study:

• To develop and validate a dictionary tool, the Self-Transcendent Emotion Dictionary (STED).
• To enable the identification and analysis of self-transcendent emotions in written language.
• To support social scientific research on positive emotions and human flourishing.

Main Methods:

• Constructed the STED using a word-counting approach.
• Validated the dictionary through three studies involving word ratings and essay analysis.
• Applied the refined STED to analyze nearly 4,000 New York Times articles.

Main Results:

• The final STED contains 351 lexicons and phrases.
• The dictionary demonstrated acceptable face, construct, and external validity.
• The STED showed applicability for analyzing positive emotions in large textual corpora.

Conclusions:

• The STED is a valuable tool for social scientific inquiry into positive emotions.
• It facilitates the quantitative analysis of self-transcendent emotions in textual narratives.
• The tool aids in understanding language related to human connectedness and flourishing.