数据库的推特影响者在加密货币 (2021-2023) 与情绪的数据库
Kia Jahanbin1, Mohammed Ali Zare Chahooki2
1Department of Computer Engineering, Yazd University, Yazd, Iran.
BMC research notes
|October 11, 2024
概括
在社交媒体上使用混合型罗伯塔-比格鲁模型分析影响者的意见,可以准确地预测加密货币价格趋势. 这种情绪分析有助于投资者进行市场预测和投资组合管理.
科学领域:
- 计算社会科学 计算社会科学
- 金融技术 (金融科技)
- 人工智能的人工智能
背景情况:
- 像Twitter这样的社交媒体平台主办的专家意见 (影响者) 显著影响公众的看法.
- 加密货币市场是不稳定的,需要先进的方法来预测趋势.
- 对专家意见的情绪分析为了解市场动态提供了一种新的方法.
研究的目的:
- 研究影响者情绪对加密货币价格趋势的预测能力.
- 开发和应用混合深度学习模型来准确分析金融推特的情绪.
- 为了分析趋势,创建一套专门的加密货币影响者意见数据集.
主要方法:
- 混合深度学习模型结合了RoBERTa (强大的优化BERT预训练方法) 和BiGRU (双向门式循环单元) 进行情绪分析.
- 一个独特的数据集被策划,包括超过52个影响者的推特关于八个加密货币,收集了八个月的时间 (2021年2月 - 2023年6月).
- 该数据集包括情绪极性,复合分数,重要性系数以及比特币,以太坊和Binance等主要加密货币的历史价格数据.
主要成果:
- 该研究表明,当与混合模型分析时,影响者情绪与加密货币价格变动相关.
- 专门的数据集允许确定特定加密货币的主导日常情绪极性.
- 罗伯塔-比格鲁模型有效地捕捉到微妙的意见从推文,区分他们从一般的基于标签的情绪.
结论:
- 在社交媒体上分享的专家意见,当通过先进的AI技术进行分析时,可以成为预测加密货币市场趋势的宝贵工具.
- 开发的情绪分析框架为寻求在加密货币市场中导航的投资者提供了可操作的见解.
- 这项研究强调了将社交媒体情报与财务数据相结合的潜力,以提高市场预测.
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