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Updated: Jun 26, 2025

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数据库对与加密货币相关的Telegram频道的评论与情绪有关
Kia Jahanbin1, Mohammad Ali Zare Chahooki2, Mahdi Yazdian-Dehkordi1
1Department of Computer Engineering, Yazd University, Yazd, Iran.
BMC research notes
|May 14, 2024
概括
这项研究使用混合深度学习模型 (HDRB) 来分析来自有影响力的Telegram频道的加密货币市场情绪. 调查结果为了解市场行为和管理投资组合提供了有价值的见解.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 金融市场分析 金融市场分析
背景情况:
- 电报频道在加密货币市场上越来越有影响力.
- 像Twitter这样的传统平台对分享专家意见有局限性.
- 专家对加密货币的情绪分析对于市场预测至关重要.
研究的目的:
- 从流行的Telegram频道中提取和分析有关加密货币的专家意见.
- 评估HDRB模型在此领域情绪分析方面的有效性.
- 创建一个有价值的数据集的专家加密货币情绪.
主要方法:
- 从2023年12月到2024年3月,通过Telegram API收集数据.
- 使用混合深度学习模型 (HDRB:RoBERTa,BiGRU,注意层) 进行情绪分析.
- 数据集包括评论文本,日期,观点和情绪极性.
主要成果:
- 从十多个有影响力的Telegram频道中提取了意见.
- 应用了HDRB模型来分析专家加密货币分析的情绪极性.
- 开发了一个在加密货币情绪分析领域罕见的独特数据集.
结论:
- HDRB模型有效地分析了来自Telegram的关于加密货币的专家情绪.
- 这项研究为了解市场动态提供了宝贵的资源.
- 调查结果有助于做出明智的投资和投资组合管理决策.
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