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    Area of Science:

    • Natural Language Processing
    • Affective Computing
    • Artificial Intelligence

    Background:

    • Emotion recognition research often overlooks the reasons for misclassification.
    • Emotion correlation, including confusion and evolution, is a key factor in recognition failures.
    • Human cognitive bias significantly influences emotion perception and expression.

    Purpose of the Study:

    • To bridge the gap between emotion recognition and emotion correlation mining in web news text.
    • To identify and analyze the laws of emotion confusion and evolution.
    • To provide a computational method for understanding emotion dynamics in text.

    Main Methods:

    • Utilized three feature types and two deep neural network models for text analysis.
    • Extracted emotion confusion laws using an orthogonal basis.
    • Evaluated emotion evolution laws through one-step, limited-step, and shortest path analyses.

    Main Results:

    • Subjective news comments frequently misclassify emotions as anger.
    • Web news comments exhibit love-anger and sadness-anger emotion circulations.
    • Objective news text is often recognized as 'love,' potentially leading to fear-joy circulations.

    Conclusions:

    • Human cognitive bias drives emotion confusion and evolution in text.
    • Understanding emotion correlation is crucial for accurate emotion recognition.
    • Findings inform applications in network sentiment analysis, social media, and human-computer interaction.