A BERT-ResNet Cross-Attention Fusion Network and Modality Utilization Assessment for Multimodal Sentiment Classification
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| Опубліковано в:: | ProQuest Dissertations and Theses (2025) |
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ProQuest Dissertations & Theses
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| 001 | 3203040236 | ||
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| 020 | |a 9798314880944 | ||
| 035 | |a 3203040236 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 66569 |2 nlm | ||
| 100 | 1 | |a Gold, Ronen G. | |
| 245 | 1 | |a A BERT-ResNet Cross-Attention Fusion Network and Modality Utilization Assessment for Multimodal Sentiment Classification | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a This study explores the growing field of Multimodal Sentiment Analysis (MSA), focusing on understanding how advanced fusion techniques can improve sentiment prediction in social media contexts. As platforms like X and TikTok continue to expand and facilitate sharing sentiment through digital media, there is an increasing need for neural network architectures that can accurately interpret sentiment across modalities. We implement a model using BERT for textual features and ResNet for visual features. A cross-attention fusion module aligns the modalities for joint representation. We conduct experiments on the MVSA-Single and MVSA-Multiple datasets, which contain over 5,000 and 17,000 labeled text-image pairs. Our research explores the interactions between modalities and proposes a sentiment classifier that builds upon and outperforms current baselines while quantifying the contribution of each modality through an intramodality utilization analysis. | |
| 653 | |a Artificial intelligence | ||
| 653 | |a Computer engineering | ||
| 653 | |a Electrical engineering | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3203040236/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3203040236/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |