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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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We feel: mapping emotion on Twitter.

Mark E Larsen, Tjeerd W Boonstra, Philip J Batterham

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    |February 21, 2015
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    Summary
    This summary is machine-generated.

    Social media analysis offers real-time mental health insights. The "We Feel" system analyzed 2.73 billion tweets, revealing mood patterns and correlating them with anxiety and suicide rates.

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    Area of Science:

    • Computational social science
    • Mental health research
    • Data science

    Background:

    • Traditional mental health data collection (surveys, WHO statistics) lacks real-time insights.
    • Social media platforms like Twitter provide a continuous stream of public emotional expression.
    • Understanding real-time emotional patterns is crucial for identifying mental health needs.

    Purpose of the Study:

    • To introduce the "We Feel" system for analyzing global and regional emotional expression.
    • To validate the system's findings against known mood variation patterns.
    • To explore correlations between expressed emotions and public health data.

    Main Methods:

    • Collected 2.73 × 10^9 emotional tweets over 12 weeks.
    • Automated annotation for emotion, geographic location, and gender.
    • Applied Principal Component Analysis (PCA) to identify dominant and modulated emotional patterns, and remove diurnal/weekly variations.

    Main Results:

    • Identified a dominant in-phase pattern across all emotions, modulated by antiphase patterns for positive and negative emotions.
    • The first three principal components explained over 90% of data variation.
    • Correlations were observed between national-level anxiety burden/suicide rates and specific emotional expressions, but not depression.

    Conclusions:

    • The "We Feel" system effectively analyzes large-scale emotional expression data from social media.
    • Real-time emotional data reveals significant patterns and can be linked to public health indicators.
    • This approach offers a valuable tool for understanding and potentially addressing mental health needs.