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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.15825 |
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| _version_ | 1866914014990172160 |
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| author | Liu, Chenghao Mahanti, Aniket Naha, Ranesh Wang, Guanghao Sbai, Erwann |
| author_facet | Liu, Chenghao Mahanti, Aniket Naha, Ranesh Wang, Guanghao Sbai, Erwann |
| contents | As cryptocurrencies gain popularity, the digital asset marketplace becomes increasingly significant. Understanding social media signals offers valuable insights into investor sentiment and market dynamics. Prior research has predominantly focused on text-based platforms such as Twitter. However, video content remains underexplored, despite potentially containing richer emotional and contextual sentiment that is not fully captured by text alone. In this study, we present a multimodal analysis comparing TikTok and Twitter sentiment, using large language models to extract insights from both video and text data. We investigate the dynamic dependencies and spillover effects between social media sentiment and cryptocurrency market indicators. Our results reveal that TikTok's video-based sentiment significantly influences speculative assets and short-term market trends, while Twitter's text-based sentiment aligns more closely with long-term dynamics. Notably, the integration of cross-platform sentiment signals improves forecasting accuracy by up to 20%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15825 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Cryptocurrency Sentiment Analysis with Multimodal Features Liu, Chenghao Mahanti, Aniket Naha, Ranesh Wang, Guanghao Sbai, Erwann Computation and Language Statistical Finance As cryptocurrencies gain popularity, the digital asset marketplace becomes increasingly significant. Understanding social media signals offers valuable insights into investor sentiment and market dynamics. Prior research has predominantly focused on text-based platforms such as Twitter. However, video content remains underexplored, despite potentially containing richer emotional and contextual sentiment that is not fully captured by text alone. In this study, we present a multimodal analysis comparing TikTok and Twitter sentiment, using large language models to extract insights from both video and text data. We investigate the dynamic dependencies and spillover effects between social media sentiment and cryptocurrency market indicators. Our results reveal that TikTok's video-based sentiment significantly influences speculative assets and short-term market trends, while Twitter's text-based sentiment aligns more closely with long-term dynamics. Notably, the integration of cross-platform sentiment signals improves forecasting accuracy by up to 20%. |
| title | Enhancing Cryptocurrency Sentiment Analysis with Multimodal Features |
| topic | Computation and Language Statistical Finance |
| url | https://arxiv.org/abs/2508.15825 |