基于 BERT 和 ResNet 的多模态情感分析
Multimodal Sentiment Analysis Based on BERT and ResNet
AI总结：
针对文本与图像特征难以有效融合的问题，提出结合 BERT 与 ResNet 并采用注意力机制融合的多模态情感分析框架，在 MAVA-single 上将最佳准确率提升至 74.5%。
摘要：
随着互联网和社交媒体的快速发展，多模态数据（文本和图像）在情感分析任务中变得越来越重要。然而，现有方法难以有效融合文本与图像特征，这限制了分析的准确性。为解决这一问题，本文提出了一种结合 BERT 和 ResNet 的多模态情感分析框架。BERT 在自然语言处理中展现出强大的文本表示能力，而 ResNet 在计算机视觉领域具有优异的图像特征提取性能。首先，使用 BERT 提取文本特征向量，并使用 ResNet 提取图像特征表示。随后，探索了多种特征融合策略，最终选择基于注意力机制的融合模型，以充分利用文本与图像之间的互补信息。在公开数据集 MAVA-single 上的实验结果表明，与仅使用 BERT 或 ResNet 的单模态模型相比，所提出的多模态模型提升了准确率和 F1 分数，达到了 74.5% 的最佳准确率。本研究不仅为多模态情感分析提供了新的思路和方法，也展示了 BERT 和 ResNet 在跨领域融合中的应用潜力。未来，将探索更先进的特征融合技术和优化策略，以进一步提高多模态情感分析的准确性和泛化能力。
英文摘要：
With the rapid development of the Internet and social media, multi-modal data (text and image) is increasingly important in sentiment analysis tasks. However, the existing methods are difficult to effectively fuse text and image features, which limits the accuracy of analysis. To solve this problem, a multimodal sentiment analysis framework combining BERT and ResNet was proposed. BERT has shown strong text representation ability in natural language processing, and ResNet has excellent image feature extraction performance in the field of computer vision. Firstly, BERT is used to extract the text feature vector, and ResNet is used to extract the image feature representation. Then, a variety of feature fusion strategies are explored, and finally the fusion model based on attention mechanism is selected to make full use of the complementary information between text and image. Experimental results on the public dataset MAVA-single show that compared with the single-modal models that only use BERT or ResNet, the proposed multi-modal model improves the accuracy and F1 score, reaching the best accuracy of 74.5%. This study not only provides new ideas and methods for multimodal sentiment analysis, but also demonstrates the application potential of BERT and ResNet in cross-domain fusion. In the future, more advanced feature fusion techniques and optimization strategies will be explored to further improve the accuracy and generalization ability of multimodal sentiment analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
