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    This study introduces enhanced feature generation and topic models for accurate emotion detection in short online messages. Accelerated algorithms improve efficiency for predicting emotions and creating topic-level emotion lexicons.

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    Area of Science:

    • Natural Language Processing
    • Computational Linguistics
    • Machine Learning

    Background:

    • Online platforms generate vast amounts of short messages containing embedded emotions.
    • Detecting emotions in short texts is challenging due to sparse feature spaces.

    Purpose of the Study:

    • To develop effective methods for emotion detection in short online messages.
    • To address the feature sparsity problem in emotion analysis of short texts.

    Main Methods:

    • Generating term groups from co-occurring contexts to enrich features.
    • Proposing two supervised topic models for accurate emotion-topic association.
    • Developing an accelerated algorithm to reduce parameter estimation time.

    Main Results:

    • Validated efficiency and effectiveness of accelerated models on three short corpora.
    • Demonstrated accurate prediction of emotions in unlabeled documents.
    • Successfully generated topic-level emotion lexicons.

    Conclusions:

    • The proposed accelerated topic models are efficient and effective for emotion detection in short messages.
    • The methods contribute to improved understanding and analysis of emotions in online communication.
    • The approach enhances feature representation for sentiment analysis tasks.