计算机科学
社会化媒体
背景(考古学)
主题模型
连贯性(哲学赌博策略)
组分(热力学)
钥匙(锁)
情报检索
数据科学
万维网
人工智能
古生物学
物理
计算机安全
量子力学
生物
热力学
作者
Junaid Rashid,Jungeun Kim,Usman Naseem
标识
DOI:10.1145/3543507.3587433
摘要
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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