Sentiment Analysis Scheme for Image-Text Comments based on Multimodal Deep Learning and Spatiotemporal Attention

Fanchang, TangYaLing, Amit Yadav, Abhishek Pratap Sah, Asif Khan

    Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

    Abstract

    A sentiment analysis scheme for image and text comments based on multimodal deep learning and spatiotemporal attention is proposed to address the issues of incomplete spatiotemporal considerations, incomplete implementation details, and cutting-edge theoretical algorithms in graphic and textual sentiment analysis schemes. The proposed model has clear layering including data preprocessing layer, modal encoding layer, modal fusion layer, sentiment classification layer, loss function and optimizer, evaluation and feedback. The implementation details of each layer are introduced. The entire scheme model incorporates Multimodal Fusion Neural Network (MFNN) deep learning and spatiotemporal attention mechanism, which makes the scheme perform well in terms of security, robustness and performance, making up for the shortcomings of existing research schemes.

    Original languageEnglish
    Title of host publication6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages614-619
    Number of pages6
    ISBN (Electronic)9798331522667
    DOIs
    Publication statusPublished - 2025
    Event6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Goathgaun, Nepal
    Duration: 7 Jan 20258 Jan 2025

    Publication series

    Name6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings

    Conference

    Conference6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025
    Country/TerritoryNepal
    CityGoathgaun
    Period7/01/258/01/25

    Bibliographical note

    Publisher Copyright:
    © 2025 IEEE.

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