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Section 1 details data collection and the methods for preprocessing, emotion labeling, and topic modeling. Section 2 reports results on emotion patterns, topic clusters, and cross-cultural comparisons. Section 3 interprets these findings through cultural theories and discusses practical implications, concluding with limitations and directions for future work.
J Med Internet Res 2026;28:e84648
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This study focuses on more immediate intervention outcomes, but the broader goal of this intervention includes the prevention of the development of serious mental health difficulties that occur over longer periods of time via the growth of emotion regulation skills taught by the activity.
JMIR Ment Health 2025;12:e65491
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Indicators of a trait orientation toward self-regulation included the Consideration of Future Consequences Scale [52], the 10-item Personality Inventory [53], the Self-Control Scale [54], and the Emotion Regulation Questionnaire [55].
Three indicators of social connectedness (social network, isolation, and loneliness) were collected, including the Oslo Social Support Scale [56], the NSHAP Social Disconnectedness Scale [57], and the UCLA 3-item Loneliness Scale [58].
JMIR Res Protoc 2025;14:e66290
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Emotion socialization can occur in direct ways, such as when parents teach children about emotions and emotion expression, as well as indirect ways, such as when parents model emotion-related behaviors and react to children’s expression of emotion [6]. Indirect emotion socialization may also occur through spillover effects from one dyad to another (ie, Spillover Hypothesis), where the emotional interactions between one dyad influence another [18].
JMIR Res Protoc 2025;14:e77364
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The content of the scenario is defined in several external configuration files (.xml) in which the experimenter can specify the logical flow of the scenario and the MCQs that include: questions given to learners, the virtual patient’s responses, the type of emotional expression and its intensity (Action Unit combinations of each emotion are defined in the other configuration file), settings of spoken utterances (eg speed and pitch), and score settings (clinical reasoning and interpersonal skills).
JMIR Serious Games 2025;13:e69347
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Span Emo [43] is a deep learning–based multilabel emotion recognition model. It analyzes text segments (spans) and classifies each span according to the emotions it conveys. The keywords associated with each emotion class are presented in Table S4 in Multimedia Appendix 1. This is particularly useful in complex texts where different parts may express different emotions.
JMIR Infodemiology 2025;5:e67333
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While some previous research has used NLP to explore broad changes in help-seekers’ positive and negative sentiment [eg, 25], in the current study we analyze a wider range of general mental states (Positive Emotion, Negative Emotion, Suffering, and Optimism) and crisis-specific mental states (Distress and Suicidality).
JMIR Form Res 2025;9:e63257
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Adolescent Emoji Use in Text-Based Messaging: Focus Group Study
…I know definitely some of my friends do this, like, if they’re typically experiencing, like, a negative emotion, they’ll like emoji spam and do a bunch of random, like, really random ones…
…People, like, sometimes, like, spam emojis because they’re, like, angry that they lost [a mobile game] or they’re excited that they won.
JMIR Form Res 2025;9:e59640
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