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Multi-Label Sentiment Analysis on 100 Languages With Dynamic Weighting for Label Imbalance.

Selim F Yilmaz, E Batuhan Kaynak, Aykut Koc

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    This study introduces a novel dynamic weighting method for cross-lingual sentiment analysis in a multi-label setting. The approach achieves state-of-the-art results across multiple languages and metrics.

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    Area of Science:

    • Natural Language Processing
    • Computational Linguistics
    • Machine Learning

    Background:

    • Cross-lingual sentiment analysis is crucial for market research, politics, and social sciences.
    • Existing methods often use static weighting, failing to adapt to class imbalance.
    • Plutchik's wheel of emotions provides a framework for multi-label sentiment classification.

    Purpose of the Study:

    • To develop an advanced cross-lingual sentiment analysis framework for multi-label classification.
    • To introduce a dynamic weighting method for improved class contribution balancing.
    • To adapt focal loss and derive optimal thresholds for enhanced performance.

    Main Methods:

    • Implemented a multi-label sentiment analysis framework.
    • Introduced a novel dynamic weighting method to balance class contributions.
    • Adapted focal loss for the multi-label setting and derived optimal class-specific thresholds.

    Main Results:

    • Achieved state-of-the-art performance in seven of nine metrics across three languages.
    • Outperformed common baselines and SemEval competition methods.
    • The single model demonstrated effectiveness in cross-lingual sentiment analysis.

    Conclusions:

    • The proposed dynamic weighting and focal loss adaptation significantly improve multi-label cross-lingual sentiment analysis.
    • The method offers a robust and efficient solution for sentiment analysis across 100 languages.
    • Publicly released code facilitates further research and development in the field.