Time series & sentiment analysis of top cryptocurrencies

• Main Author: Yeoh, Chester Fu Soon
• Other Authors: Anwitaman Datta
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
• Online Access: https://hdl.handle.net/10356/148200
• Institution: Nanyang Technological University

Bitcoin, the world’s first cryptocurrency, was created in 2009 by Satoshi Nakamoto. Frustrated by the need of a central financial institute to oversee online payments made from one party to another, Bitcoin would use a blockchain framework that would be “powered by users with no central authority or middlemen” .Fast forward close to a decade later, Bitcoin has seen a meteoric rise in its popularity, exceeding 300,000 transactions per day (Appendix A). Overall, it has also blossomed into a booming market worth over USD 1 Tn (as of 9 March 2021) and ushered in a new era of cryptocurrencies. The benefits of cryptocurrencies such as accessibility and user autonomy (i.e., lack of an intermediary authority) (Reiff, 2021) are well documented. However, most cryptocurrencies suffer from a key drawback - high price fluctuations. Bitcoin, for example, increased its value by 1900% in 2017 before plunging below USD 8,000 per coin up from its high of USD 19,000 in the 2018 Jan/Feb crash. Since cryptocurrency price research is still a relatively unexplored area and equilibrium market prices are poorly understood (Conrad, 2018), this project seeks to conduct a sentiment analysis to explore possible drivers such as social media and news reports that leads to these price fluctuations. Additionally, other non-sentiment factors such as Public Interest, Community Data and Developer Data will also be analysed for their respective price impacts. Under the sentiment analysis portion of this FYP, Reddit and Twitter social media content & articles from 30,000 news sources and blogs will be mined and analysed using a Valence Aware Dictionary for Sentiment Reasoning (VADER) model to facilitate a correlation analysis against prices of top cryptocurrencies such as Bitcoin (BTC) and Ethereum (ETH). Non-sentiment factors are also extracted as inputs for building a Linear Regression model as well as a Random Forest Classifier. The FYP results indicate that there are weak correlations (|r|< 0.1) with news and social media sentiments across both cryptocurrencies. On the other hand, non-sentiment factors were strongly correlated with price across the board (|r|>0.7) and specific factors such as Number of Twitter Followers for a cryptocurrency’s Twitter account (i.e. @Bitcoin) and Number of Reddit Accounts following a cryptocurrency’s subreddit (i.e. r/Bitcoin) appeared to be strong price drivers under the high-accuracy regression and classification models.