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Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
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Related Experiment Video

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Brain Imaging Investigation of the Neural Correlates of Emotion Regulation
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Public Behavior and Emotion Correlation Mining Driven by Aspect From News Corpus.

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    This study reveals how public emotions, like fear and sadness, drive specific behaviors such as panic. Understanding these emotion-behavior links offers insights for public safety and human-computer interaction.

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    Area of Science:

    • Computational Social Science
    • Natural Language Processing
    • Artificial Intelligence

    Background:

    • Emotion significantly influences human behavior, yet the direct correlation between public emotion and behavior is under-explored.
    • Understanding these links is crucial for deciphering behavioral motives and social event dynamics.

    Purpose of the Study:

    • To propose a novel methodology for mining correlations between public behavior and emotion using daily news data.
    • To develop a knowledge representation model based on hypothetical context (KRHC) for uncovering implicit emotion-behavior relationships.

    Main Methods:

    • Extraction and generalization of aspect-emotion-reaction (A-E-R) triplets from news data.
    • Development of the KRHC model with a self-reflection mechanism and attention to capture implicit patterns.
    • Integration of rule-based methods for explicit relationships and deep learning for implicit ones.

    Main Results:

    • The KRHC model effectively uncovers implicit emotion-behavior relationships.
    • Validation on A-E-R and KINSHIP datasets demonstrates the model's efficacy.
    • Identified specific emotion-behavior correlations, e.g., fear, sadness, and surprise correlating with panic behavior.

    Conclusions:

    • The proposed methodology provides a robust framework for analyzing public emotion-behavior dynamics.
    • Findings offer valuable insights for applications in human-computer interaction and public safety management.
    • This research highlights the significance of analyzing public sentiment for predicting and understanding collective behavior.