Developing and Validating the Self-Transcendent Emotion Dictionary for Textual Analysis

Recent years have seen a growing amount of research effort directed toward what positive media psychologists refer to as self-transcendent emotions, such as awe, admiration, elevation, gratitude, inspiration, and hope. While these emotions are invaluable to promote greater human connectedness, prosociality, and human flourishing, researchers are constrained in terms of analyzing self-transcendent emotions as expressed in spoken and written languages. In response, we constructed a computational tool—Self-Transcendent Emotion Dictionary (STED)—which can be uploaded into mainstream, textual analytic software (e.g., LIWC) to identify and analyze self-transcendent emotions in large corpora both effectively and efficiently. This dictionary tool was then refined and validated via three consecutive studies. Potential applications for the newly developed dictionary instrument and what it could mean for future studies of media psychology are discussed.

近年来，学界已投入越来越多的研究精力，聚焦于积极媒体心理学研究者所称的自我超越情绪（self-transcendent emotions），包括敬畏（awe）、钦佩（admiration）、崇高感（elevation）、感恩（gratitude）、灵感（inspiration）与希望（hope）。尽管这类情绪对于强化人际联结、推动亲社会行为以及促进人类福祉具有不可估量的价值，但研究者在分析口语与书面语中所表达的自我超越情绪时仍面临诸多局限。为此，我们研发了一款计算工具——自我超越情绪词典（Self-Transcendent Emotion Dictionary，简称STED），该工具可上传至主流文本分析软件（如LIWC），从而高效且精准地识别并分析大型语料库中的自我超越情绪。随后，我们通过三项连续开展的研究对该词典工具进行了优化与效度验证。本文最后讨论了这款新研发词典工具的潜在应用场景，以及其对未来媒体心理学研究的重要意义。