Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis

Article


Bhatt, S., Ghazanfar, M. and Amirhosseini, M. 2023. Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis. Machine Learning and Applications: An International Journal (MLAIJ). 10 (2/3), pp. 1-15. https://doi.org/10.5121/mlaij.2023.10301
AuthorsBhatt, S., Ghazanfar, M. and Amirhosseini, M.
Abstract

This research explores the impact of social media sentiments on predicting Bitcoin prices using machine learning models, integrating on-chain data, and applying a Multi Modal Fusion Model. Historical crypto market, on-chain, and Twitter data from 2014 to 2022 were used to train models including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting, and Multi Modal Fusion. Performance was compared with and without Twitter sentiment data which was analysed using the Twitter-roBERTa and VADAR models. Inclusion of sentiment data enhanced model performance, with Twitter-roBERTa-based models achieving an average accuracy score of 0.81. The best performing model was an optimised Multi Modal Fusion model using Twitter-roBERTa, with an accuracy score of 0.90. This research underscores the value of integrating social media sentiment analysis and on-chain data in financial forecasting, providing a robust tool for informed decision-making in cryptocurrency trading.

KeywordsCryptocurrency; Bitcoin Price; Social Media; Sentiment Analysis; Machine Learning; Classification
JournalMachine Learning and Applications: An International Journal (MLAIJ)
Journal citation10 (2/3), pp. 1-15
ISSN2394-0840
Year2023
PublisherAIRCC Publishing Corporation
Publisher's version
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.5121/mlaij.2023.10301
Web address (URL)https://aircconline.com/abstract/mlaij/10323mlaij01.html
Publication dates
OnlineSep 2023
Publication process dates
Accepted10 Jul 2023
Deposited11 Jul 2023
Copyright holder© 2023, AIRCC Publishing Corporation
Permalink -

https://repository.uel.ac.uk/item/8w48q

Download files


Publisher's version
SENTIMENT-DRIVEN CRYPTOCURRENCY PRICE PREDICTION.pdf
License: All rights reserved
File access level: Anyone

  • 412
    total views
  • 347
    total downloads
  • 39
    views this month
  • 76
    downloads this month

Export as

Related outputs

A Graph-Based Method for Identity Resolution to Assist Police Force Investigative Process
Amirhosseini, M., Kazemian, H. and Phillips, M. 2024. A Graph-Based Method for Identity Resolution to Assist Police Force Investigative Process. Journal of Cyber Security and Technology. In Press. https://doi.org/10.1080/23742917.2024.2354555
An AI Powered System to Detect Autism Spectrum Disorder in Toddlers
Amirhosseini, M.H., Alam, N, Kalabi, F. and Virdee, B. 2024. An AI Powered System to Detect Autism Spectrum Disorder in Toddlers . ICDAM 2024: 5th International Conference on Data Analytics & Management. London, UK 14 - 15 Jun 2024 Springer.
Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning
Valizadeh, A., Amirhosseini, M. H. and Ghorbani, Y. 2024. Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning. Computers and Chemical Engineering. 183 (Art. 108623). https://doi.org/10.1016/j.compchemeng.2024.108623
An artificial intelligence approach to predicting personality types in dogs
Amirhosseini, M. H., Yadav, V., Serpell, J. A., Pettigrew, P. and Kain, P. 2024. An artificial intelligence approach to predicting personality types in dogs. Scientific Reports. 14 (Art. 2404). https://doi.org/10.1038/s41598-024-52920-9
A reinforcement learning recommender system using bi-clustering and Markov Decision Process
Iftikhar, A., Ghazanfar, M. A., Ayub, M., Alahmari, S. A., Qazi, N. and Wall, J. 2024. A reinforcement learning recommender system using bi-clustering and Markov Decision Process. Expert Systems with Applications. 237 (Art.), p. 121541. https://doi.org/10.1016/j.eswa.2023.121541
Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches
Sontakke, P., Jafari, F., Saeedi, M. and Amirhosseini, M. 2024. Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches. ICDAM-2023: 4th International Conference on Data Analytics & Management. London, UK 23 - 24 Jun 2023 Springer. https://doi.org/10.1007/978-981-99-6544-1_7
Shifting the Weight: Applications of AI in Olympic Weightlifting
Bolarinwa, D., Qazi, N. and Ghazanfar, M. 2023. Shifting the Weight: Applications of AI in Olympic Weightlifting. PRDC 2023: 28th IEEE Pacific Rim International Symposium on Dependable Computing. Singapore 24 - 27 Oct 2023 IEEE. https://doi.org/10.1109/PRDC59308.2023.00051
An AI powered system to enhance self-reflection practice in coaching
Jelodari, M., Amirhosseini, M. H. and Giraldez Hayes, A. 2023. An AI powered system to enhance self-reflection practice in coaching. Cognitive Computation and Systems. 5 (4), pp. 243-254. https://doi.org/10.1049/ccs2.12087
Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment
Bhatt, S., Ghazanfar, M. and Amirhosseini, M. 2023. Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment . 5th International Conference on Machine Learning & Applications (CMLA 2023). Sydney, Australia 17 - 18 Jun 2023 AIRCC Publishing Corporation.
Large-Scale Music Genre Analysis and Classification Using Machine Learning with Apache Spark
Chaudhury, M., Karami, A. and Ghazanfar, M. A. 2022. Large-Scale Music Genre Analysis and Classification Using Machine Learning with Apache Spark. Electronics. 11 (16), p. 2567. https://doi.org/10.3390/electronics11162567
A Machine Learning Approach to Identify the Preferred Representational System of a Person
Amirhosseini, M. and Wall, J. 2022. A Machine Learning Approach to Identify the Preferred Representational System of a Person. Multimodal Technologies and Interaction. 6 (12), p. 112. https://doi.org/10.3390/mti6120112
Application of Graph-Based Technique to Identity Resolution
Kazemian, H., Amirhosseini, M. H. and Phillips, M. 2022. Application of Graph-Based Technique to Identity Resolution. AIAI 2022: 18th International Conference on Artificial Intelligence Applications and Innovations. Crete, Greece 17 - 20 Jun 2022 Springer. https://doi.org/10.1007/978-3-031-08333-4_38
A novel DeepMaskNet model for face mask detection and masked facial recognition
Ullah, N., Javed, A., Ghazanfar, M., Alsufyani, A. and Bourouis, S. 2022. A novel DeepMaskNet model for face mask detection and masked facial recognition. Journal of King Saud University - Computer and Information Sciences. 30 (10-B), pp. 9905-9914. https://doi.org/10.1016/j.jksuci.2021.12.017
Asset Criticality and Risk Prediction for an Effective Cyber Security Risk Management of Cyber Physical System
Kure, H. I., Islam, S., Ghazanfar, M., Raza, A. and Pasha, M. 2021. Asset Criticality and Risk Prediction for an Effective Cyber Security Risk Management of Cyber Physical System. Neural Computing and Applications. 34, p. 493–514. https://doi.org/10.1007/s00521-021-06400-0
Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)
Khalid, A., Lundqvist, K., Yates, A. and Ghazanfar, M. 2021. Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs). PLoS ONE. 16 (Art. e0245485). https://doi.org/10.1371/journal.pone.0245485
Stock market prediction using machine learning classifiers and social media, news
Khan, W., Ghazanfar, M., Azam, M. A., Karami, A., Alyoubi, K. H. and Alfakeeh, A. S. 2020. Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing. 13, pp. 3433-3456. https://doi.org/10.1007/s12652-020-01839-w
A novel centroids initialisation for K-means clustering in the presence of benign outliers
Karami, A., Ur Rehman, S. and Ghazanfar, M. 2020. A novel centroids initialisation for K-means clustering in the presence of benign outliers. International Journal of Data Analysis Techniques and Strategies. 12 (4), pp. 287-298. https://doi.org/10.1504/IJDATS.2020.111498
Identifying Users with Wearable Sensors based on Activity Patterns
Ehatisham-ul-Haq, M., Malik, M. N., Azam, M. A., Naeem, U., Khalid, A. and Ghazanfar, M. 2020. Identifying Users with Wearable Sensors based on Activity Patterns. The 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2020). Madeira, Portugal 02 - 05 Nov 2020 Elsevier. https://doi.org/10.1016/j.procs.2020.10.005
A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data
Phillips, M., Amirhosseini, M. and Kazemian, H. 2020. A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data. 2020 IEEE Symposium Series on Computational Intelligence. Online 01 - 04 Dec 2020 IEEE. https://doi.org/10.1109/SSCI47803.2020.9308182
Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®
Amirhosseini, M.H. and Kazemian, H. 2020. Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®. Multimodal Technologies and Interaction. 4 (Art. 9). https://doi.org/10.3390/mti4010009
Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems
Ayub, M., Ghazanfar, M., Mehmood, Z., Saba, T., Alharbey, R., Munshi, A. M. and Alrige, M. A. 2019. Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems. PLoS ONE. 14 (Art. e0220129). https://doi.org/10.1371/journal.pone.0220129
Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing
Amirhosseini, M.H. and Kazemian, H. 2019. Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing. Cognitive Processing. 20 (2), p. 175–193. https://doi.org/10.1007/s10339-019-00912-3
Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm
Iqbal, Misbah, Ghazanfar, M., Sattar, Asma, Maqsood, Muazzam, Khan, Salabat, Mehmood, Irfan and Baik, Sung Wook 2019. Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm. IEEE Access. 7, pp. 24719-24737. https://doi.org/10.1109/ACCESS.2019.2897003
Natural Language Processing approach to NLP Meta model automation
Amirhosseini, M.H., Kazemian, H., Ouazzane, K. and Chandler, C. 2018. Natural Language Processing approach to NLP Meta model automation. 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil 08 - 13 Jul 2018 IEEE. https://doi.org/10.1109/IJCNN.2018.8489609
A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets
Khan, U., Aadil, F., Ghazanfar, M., Khan, S., Metawa, N., Muhammad, K., Mehmood, I. and Nam, Y. 2018. A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets. Sustainability. 10 (Art. 3702). https://doi.org/10.3390/su10103702