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Sentiment Analysis: Comprehensive Reviews, Recent Advances, and Open Challenges.

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    This study provides a comprehensive overview of sentiment analysis (SA), focusing on multimodal approaches. It details single-modal SA methods and introduces a new taxonomy for multimodal SA (MSA) to guide future research.

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    Area of Science:

    • Artificial Intelligence
    • Natural Language Processing
    • Computer Science

    Background:

    • Sentiment Analysis (SA) has advanced significantly, with applications in public opinion and voice services.
    • Deep learning has spurred research in multimodal SA (MSA), but comprehensive surveys are lacking.
    • Existing studies often analyze single modalities separately, hindering a holistic understanding of MSA.

    Purpose of the Study:

    • To systematically review and categorize existing SA methods, with a focus on multimodal approaches.
    • To provide a framework for understanding SA tasks based on data modalities.
    • To identify research gaps and propose future directions in MSA.

    Main Methods:

    • A novel framework is proposed, organizing SA tasks by modality.
    • Detailed review of single-modal SA workflows, data processing, and modeling.
    • Introduction of a new taxonomy for MSA, covering representation learning and data fusion.

    Main Results:

    • Comprehensive analysis of single-modal SA, highlighting similarities and differences to inform MSA.
    • Detailed discussion of multimodal representation learning and data fusion techniques.
    • Exploration of advanced topics like multimodal alignment, chatbots, and ChatGPT in SA.

    Conclusions:

    • The article offers a structured overview of SA and MSA, addressing current research gaps.
    • Identifies key areas for future research, including cross-modal contrastive learning and pretraining models.
    • Provides guidance for researchers on datasets and methods for various SA tasks.