社交媒体上创造性多模态数据的连贯主题建模

Junaid Rashid, Jungeun Kim, Usman Naseem
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引用次数: 0

摘要

创意网络就是将不同类型的媒体结合起来,创造一种独特的、引人入胜的在线体验。多模态数据,如文本和图像,是创意网络的关键组成部分。结合文字描述和图片的社交媒体帖子提供了丰富的信息和背景。社交媒体帖子中的文本通常与一个主题相关,而由于视觉内容的丰富性,图像通常传达多个主题的信息。尽管有这种潜力,但许多现有的多模态主题模型没有考虑到这些标准,导致生成的主题质量很差。因此,我们提出了针对多模态数据的连贯主题建模(CTM-MM),该模型考虑到社交媒体帖子中的文本通常与一个主题相关,而图像可以包含多个主题的信息。实验结果表明,CTM-MM在分类和主题一致性方面优于传统的多模态主题模型。
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Coherent Topic Modeling for Creative Multimodal Data on Social Media
The creative web is all about combining different types of media to create a unique and engaging online experience. Multimodal data, such as text and images, is a key component in the creative web. Social media posts that incorporate both text descriptions and images offer a wealth of information and context. Text in social media posts typically relates to one topic, while images often convey information about multiple topics due to the richness of visual content. Despite this potential, many existing multimodal topic models do not take these criteria into account, resulting in poor quality topics being generated. Therefore, we proposed a Coherent Topic modeling for Multimodal Data (CTM-MM), which takes into account that text in social media posts typically relates to one topic, while images can contain information about multiple topics. Our experimental results show that CTM-MM outperforms traditional multimodal topic models in terms of classification and topic coherence.
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