Convergence of Twitter Sentiment Analysis and Optimized Learning Models for Predicting Bitcoin Price Volatility

Abstract

Bitcoin has attained increasing recognition and interest from individuals and corporations, with more than $1 billion market capitalization. Twitter users’ sentiment on the topic is a major factor that influences volatility of Bitcoin’s price. Compared to other financial markets, there are a limited number of studies that discuss the price fluctuation prediction of Bitcoin using Twitter sentiment. A dataset with 16 million tweets from August 2018 to October 2019 was utilized for finding the correlation between the daily close price of Bitcoin and Twitter sentiment. This dataset was pre-processed by following steps such as removing null, duplicate and non-English tweets. The sentiment analysis was carried out using VADER sentiment analyzer. This research utilized hyperparameter optimization and improved two deep learning models (with Long Short-Term Memory and Convolutional Neural Network architectures), for the tasks of direction and magnitude prediction with accuracies of 82.35% and 72.06%, respectively on test datasets. With hyperparameter optimization this research addresses a gap in the existing research of this research area, which was not utilizing hyperparameter optimization to improve deep learning models.