[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

To read this content please select one of the options below:

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

Muhammad Yasir (Department of Management Sciences, COMSATS University Islamabad- Attock Campus, Attock, Pakistan)
Muhammad Attique (Department of Software, Sejong University, Seoul, Korea)
Khalid Latif (Department of Commerce, Government College University Faisalabad, Faisalabad, Pakistan)
Ghulam Mujtaba Chaudhary (Department of Business Administration, University of Kotli Azad Jammu and Kashmir, Kotli, Pakistan)
Sitara Afzal (Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, Pakistan)
Kamran Ahmed (Department of Management Sciences, University of Wah, Wah Cantonment, Pakistan)
Farhan Shahzad (Department of Management Sciences, University of Wah, Wah Cantonment, Pakistan)

Journal of Enterprise Information Management

ISSN: 1741-0398

Article publication date: 2 February 2021

Issue publication date: 24 April 2023

897

Abstract

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.

Keywords

Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (2020R1G1A1013221).The authors Muhammad Yasir and Muhammad Attique contributed significantly and are considered as co-first authors.

Citation

Yasir, M., Attique, M., Latif, K., Chaudhary, G.M., Afzal, S., Ahmed, K. and Shahzad, F. (2023), "Deep-learning-assisted business intelligence model for cryptocurrency forecasting using social media sentiment", Journal of Enterprise Information Management, Vol. 36 No. 3, pp. 718-733. https://doi.org/10.1108/JEIM-02-2020-0077

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

Related articles