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- Creators: Department of Supply Chain Management
Description
Cryptocurrencies are notorious for its volatility. But with its incredible rise in price, Bitcoin keep being on the top among the trending topics on social media. Although doubts continue to rise with price, Bloomberg even make critics on Bitcoin as ‘the biggest bubble in the history’, some investors still hold strong enthusiasm and confidence towards Bitcoin. As contradicting opinions increase, it is worthy to dive into discussions on social media and use a scientific method to evaluate public’s non-negligible role in crypto price fluctuation.
Sentiment analysis, which is a notably method in text mining, can be used to extract the sentiment from people’s opinion. It then provides us with valuable perception on a topic from the public’s attitude, which create more opportunities for deeper analysis and prediction.
The thesis aims to investigate public’s sentiment towards Bitcoin through analyzing 10 million Bitcoin related tweets and assigning sentiment points on tweets, then using sentiment fluctuation as a factor to predict future crypto fluctuation. Price prediction is achieved by using a machine learning model called Recurrent Neural Network which automatically learns the pattern and generate following results with memory. The analysis revels slight connection between sentiment and crypto currency and the Neural Network model showed a strong connection between sentiment score and future price prediction.
Sentiment analysis, which is a notably method in text mining, can be used to extract the sentiment from people’s opinion. It then provides us with valuable perception on a topic from the public’s attitude, which create more opportunities for deeper analysis and prediction.
The thesis aims to investigate public’s sentiment towards Bitcoin through analyzing 10 million Bitcoin related tweets and assigning sentiment points on tweets, then using sentiment fluctuation as a factor to predict future crypto fluctuation. Price prediction is achieved by using a machine learning model called Recurrent Neural Network which automatically learns the pattern and generate following results with memory. The analysis revels slight connection between sentiment and crypto currency and the Neural Network model showed a strong connection between sentiment score and future price prediction.
ContributorsZhu, Xiaoyu (Author) / Benjamin, Victor (Thesis director) / Qinglai, He (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
This report is a summary of a long-term project completed by Ido Gilboa for his Honors Thesis. The purpose of this project is to determine if an arbitrage between different crypto-currency exchanges exists, and if it is possible to acts upon such triangular arbitrage. Bitcoin, the specific crypto-currency this report focuses on, has become a household name, yet most do not understand its origin and patterns. The report will detail the process of collecting data from different sources, manipulating it in order to run the algorithms, explain the meaning behind the algorithms, results and important statistics found, and conclusion of the project. In addition to that, the report will go into detail discussing financial terms such as triangular arbitrage as well as information system concepts such as sockets and server communication. The project was completed with the assistance of Dr. Sunil Wahal and Dr. Daniel Mazzola, professors in the W.P. Carey School of business. This project has been stretched over along period of time, spanning from early 2013 to fall of 2015.
ContributorsGilboa, Ido (Author) / Wahal, Sunil (Thesis director) / Mazzola, Daniel (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12