Deep-learning-assisted business intelligence model for cryptocurrency forecasting using social media sentiment

PurposeBusiness 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 t...

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Bibliographic Details
Published in:Journal of enterprise information management 2023-04, Vol.36 (3), p.718-733
Main Authors: Yasir, Muhammad, Attique, Muhammad, Latif, Khalid, Chaudhary, Ghulam Mujtaba, Afzal, Sitara, Ahmed, Kamran, Shahzad, Farhan
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Attraction
Business intelligence
Consumer Price Index
Datasets
Decision making
Deep learning
Digital currencies
Digital media
Econometrics
Forecasting
Foreign exchange rates
Information management
Intelligence (information)
Literature reviews
Machine learning
Macroeconomics
Mathematical models
Model accuracy
Neural networks
Optimization techniques
Performance enhancement
Regression analysis
Sentiment analysis
Social networks
Support vector machines
Time series
Volatility
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Summary:PurposeBusiness 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/approachThis 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.FindingsThe 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/valueThe use of social media sentiment of global and local events for different countries along with deep learning for CC forecasting.
ISSN:1741-0398
1758-7409
DOI:10.1108/JEIM-02-2020-0077