Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment - CORE Reader
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We trained various models over historical data including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boostingand a Multi Modal Fusion The purpose of this research is to investigate the impact of social media sentiments on predict
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- We trained various models over historical data including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boostingand a Multi Modal Fusion
- The purpose of this research is to investigate the impact of social media sentiments on predicting the Bitcoin price using machine learning models, with a focus on integratingon-chain data and employing a Multi Modal Fusion Model
- Next, we added Twitter sentiment data to the models, using the Twitter-roBERTa and VADAR models to analyse the sentiments expressed in social media about Bitcoin. We then compared the performance of these models with and without the Twitter sentiment data and found that the inclusion of sentiment feature resulted in consistently better performance,...
- The best performing model was an optimised Multi Modal Fusion classifier using Twitter-RoBERTa based sentiment, producing an F1 score of 0.85.
- The goal of this study isto develop a machine learning-based model for predicting the price of Bitcoin using market and on-chain data, with a focus on Twitter sentiment analysis
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