With the rapid development of internet technology, the social media sector has seen an increasing amount of multimodal data, including text, images, and videos, that contain a large amount of useful information. These data play an extremely important role in sentiment analysis, personalized recommendations, and public opinion monitoring. Both in industry and academia, the field of multimodal data has received unprecedented attention. This paper focuses on the problem of multimodal classification of text and images in the social media sector. From the perspectives of tensor feature fusion and neural network feature fusion, two novel models for multimodal classification are proposed.

你是一个中英文翻译专家。我现在需要对一段中文进行翻译翻译成英文。内容如下:随着互联网技术的飞速发展社交媒体领域出现了越来越多的多模态数据。这些多模态数据包含文本、图像和视频等大量有用信息在情感分析、个性化推荐和舆情监控等方面都具有极其重要的作用。不论是在工业界还是在学术界多模态领域都受到了前所未有的关注。本文的研究主题是社交媒体领域的图文多模态分类问题从张量特征融合和神经网络特征融合两个角度出发提

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