一个基于群体优化的情绪分析的融合模型,用于加密货币价格预测
Dimple Tiwari1, Bhoopesh Singh Bhati2, Bharti Nagpal3
1School of Engineering & Technology, Vivekananda Institute of Professional Studies - Technical Campus, Delhi, India.
Scientific reports
|March 8, 2025
概括
一个新的优化堆叠长短期记忆 (LSTM) 模型增强了用于加密货币价格预测的社会情绪分析. 这种先进的模型处理多语言数据,为金融市场预测提供了更高的准确性和概括性.
科学领域:
- 计算语言学 计算语言学
- 人工智能的人工智能
- 金融技术 金融技术
背景情况:
- 社交媒体产生了大量的非结构化数据,影响了包括金融在内的各个部门.
- 从杂的社交媒体数据中提取可靠的消费者情绪存在重大挑战.
- 对于社会情绪分析的现有整体模型往往缺乏可预测性和概括性.
研究的目的:
- 为加密货币价格预测提出一个基于优化堆叠的长短期内存 (LSTM) 的情绪分析模型.
- 增强模型从社交媒体文本中捕获语义和统计特征的能力.
- 建立一个基准情绪分析模型,用于预测加密货币价格和其他社会情绪.
主要方法:
- 开发了一个优化的堆叠LSTM架构,具有多个层.
- 利用粒子群集优化 (PSO) 来优化每个LSTM层的超参数.
- 处理多语言和跨平台社交媒体数据的内置技术,包括多语言嵌入和微调.
主要成果:
- 优化堆叠的LSTM模型与现有方法相比,表现出卓越的性能.
- 该模型有效地确定了上下文语义和共发生统计特征之间的关系.
- 使用诸如混矩阵,加权f1-Score,精度,回忆和准确性等指标评估模型效率.
结论:
- 建议的优化堆叠LSTM模型为社会情绪分析提供了更可预测和更普遍的方法.
- 该模型处理多语言和跨平台数据的能力使其成为加密货币价格预测的强大工具.
- 这项研究为金融市场情绪分析和更广泛的社会应用提供了有价值的基准.
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