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 language | English |
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Title of host publication | 6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 614-619 |
Number of pages | 6 |
ISBN (Electronic) | 9798331522667 |
DOIs | |
Publication status | Published - 2025 |
Event | 6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Goathgaun, Nepal Duration: 7 Jan 2025 → 8 Jan 2025 |
Publication series
Name | 6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings |
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Conference
Conference | 6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 |
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Country/Territory | Nepal |
City | Goathgaun |
Period | 7/01/25 → 8/01/25 |
Bibliographical note
Publisher Copyright:© 2025 IEEE.