融合视频与文本数据，提升加密货币情绪分析精度。
Enhancing Cryptocurrency Sentiment Analysis with Multimodal Features
- 结合TikTok视频与Twitter文本，多模态分析情绪信号。
- 视频情绪对短期市场影响更大，文本情绪关联长期趋势。
- 跨平台情绪融合使预测准确率提升20%。
随着加密货币日益普及，数字资产市场愈发重要。社交媒体信号为理解投资者情绪和市场动态提供了宝贵洞见。以往研究主要聚焦于推特等文本平台，但视频内容仍被忽视，尽管其可能蕴含更丰富的感情与上下文信息，这些是纯文本难以捕捉的。本研究通过大语言模型，对比分析TikTok与推特的情绪特征，从视频与文本中提取洞察。我们探究了社交媒体情绪与加密货币市场指标之间的动态依赖与溢出效应。结果表明，TikTok的视频情绪显著影响投机性资产及短期市场走势，而推特的文本情绪则更贴近长期动态。值得注意的是，跨平台情绪信号的整合使预测准确率最高提升20%。
原文摘要 · Abstract (English)
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%.
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