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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Visual Sentiment Analysis With Social Relations-Guided Multiattention Networks.

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    This study introduces a new model for visual sentiment analysis that considers both image details and social connections. The social relations-guided multiattention networks (SRGMANs) model improves emotion detection in social media images.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Social Media Analysis

    Background:

    • Social media users express emotions through images, posing research challenges for sentiment analysis.
    • Existing methods often overlook multilevel visual features (region/object) and inter-image correlations.

    Purpose of the Study:

    • To propose a novel model, social relations-guided multiattention networks (SRGMANs), for enhanced visual sentiment analysis.
    • To integrate multilevel visual features and social image correlations for improved emotion detection.

    Main Methods:

    • Constructed a heterogeneous network of social relations and used network embedding for image representation.
    • Developed region and object attention networks with self-attention and network-guided attention modules.
    • Combined multilevel visual features and network representations for sentiment prediction.

    Main Results:

    • The proposed SRGMANs model demonstrated superior performance in visual sentiment analysis.
    • Experiments on three benchmark datasets validated the model's effectiveness.

    Conclusions:

    • Integrating multilevel visual features and social correlations significantly improves visual sentiment analysis.
    • The SRGMANs model offers a promising approach for understanding emotions in social images.