用聊天情绪+金融数据预测虚拟币价格，效果优于纯价格模型。
Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token
- 用BERT分析社区聊天情绪，融合价格、交易量等多模态数据。
- 多模态模型预测准确率显著高于仅用历史价格的基线模型。
- 适合对虚拟经济、NLP与加密货币交叉研究感兴趣的人。
Decentraland 是一个运行在不断扩展的元宇宙生态系统中的去中心化虚拟现实平台，其原生 MANA 代币用于虚拟资产交易和治理。本研究探讨将 Discord 社区情绪与多模态金融数据结合，以提升虚拟世界经济体中加密货币价格预测的准确性。研究解决两个问题：(1) 识别 Decentraland Discord 社区内的情绪模式；(2) 评估多模态特征对代币收益率预测的影响。采用基于 BERT 的大语言模型进行情感分析，构建两种 LSTM 架构：一种仅包含历史价格的基线模型，另一种整合情绪得分、交易量和市值的多模态模型。结果表明社区情绪总体中性但偏正面。多模态模型在预测准确性上显著优于仅使用价格的基线模型。这些发现证明了社区生成信号在虚拟经济预测中的价值，并为沉浸式虚拟环境、自然语言处理与加密货币市场分析的交叉研究奠定了基础。
原文摘要 · Abstract (English)
Decentraland, a decentralized virtual reality platform operating within the expanding Metaverse ecosystem, utilizes its native MANA token to facilitate virtual asset transactions and governance. This study investigates the integration of Discord community sentiment with multi-modal financial data to enhance cryptocurrency price prediction within virtual world economies. We address: (1) identifying sentiment patterns within Decentraland's Discord community, and (2) evaluating the impact of multi-modal features on token return forecasting. Using a BERT-based large language model for sentiment analysis, we develop two LSTM architectures: a baseline incorporating historical prices and a multi-modal variant integrating sentiment scores, trading volume, and market capitalization. Results indicate predominantly neutral community sentiment with a positive skew. The multi-modal model significantly outperforms the price-only baseline in prediction accuracy. These findings demonstrate the predictive value of community-derived signals for virtual economy forecasting and establish a foundation for future research at the intersection of immersive virtual environments, natural language processing, and cryptocurrency market analysis.
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