Abstract
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.
Original language | English |
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Title of host publication | The ACM Web Conference 2023 |
Subtitle of host publication | proceedings of the World Wide Web Conference WWW 2023 |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc |
Pages | 3923-3927 |
Number of pages | 5 |
ISBN (Electronic) | 9781450394161 |
DOIs | |
Publication status | Published - 2023 |
Externally published | Yes |
Event | 2023 World Wide Web Conference, WWW 2023 - Austin, United States Duration: 30 Apr 2023 → 4 May 2023 |
Conference
Conference | 2023 World Wide Web Conference, WWW 2023 |
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Country/Territory | United States |
City | Austin |
Period | 30/04/23 → 4/05/23 |
Keywords
- Topic Modeling
- Multimodal
- Creative Web
- Coherence