Purpose

Business Intelligence has gained a significant attraction in the recent past and facilitates managers for efficient business decision-making. Over the years, the attraction toward the cryptocurrency (CC) market has increased. Since the CC market is highly volatile, it is extremely sensitive to shocks and web data related to large events happening around the globe.

Design/methodology/approach

This research study provides a business intelligence model to predict five top-performing CCs. In this study, deep learning, linear regression and support vector regression (SVR) are used to predict CC prices. The sentiment of some mega-events is also used to enhance the performance of these models.

Findings

The results show that models of business intelligence such as deep learning and SVR provide better results. Moreover, the results show that the incorporation of social media sentiment data significantly improves the performance of the proposed models. The overall accuracy of the model improves approximately twofold when multiple event sentiments were incorporated.

Originality/value

The use of social media sentiment of global and local events for different countries along with deep learning for CC forecasting.

中文翻译：

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用于使用社交媒体情绪进行加密货币预测的深度学习辅助商业智能模型

目的

商业智能在最近获得了巨大的吸引力，并有助于管理人员进行有效的业务决策。多年来，对加密货币 (CC) 市场的吸引力有所增加。由于 CC 市场高度波动，它对冲击和与全球发生的大型事件相关的网络数据极为敏感。

设计/方法/途径

这项研究提供了一个商业智能模型来预测五个表现最好的 CC。在本研究中，深度学习、线性回归和支持向量回归 (SVR) 用于预测 CC 价格。一些大型事件的情绪也被用来增强这些模型的性能。

发现

结果表明，深度学习和 SVR 等商业智能模型提供了更好的结果。此外，结果表明，社交媒体情绪数据的结合显着提高了所提出模型的性能。当合并多个事件情绪时，模型的整体准确性提高了大约两倍。

原创性/价值

使用不同国家/地区的全球和本地事件的社交媒体情绪以及 CC 预测的深度学习。