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- All Subjects: Bitcoin
- Creators: Benjamin, Victor
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
Bitcoin is a form of virtual currency that can be used as a medium of exchange for goods or services. Different from other forms of virtual payment, bitcoin is de-centralized and puts all of the power in the hands of the user, rather than a banking institution. However, bitcoin's ability to develop as a renowned medium of exchange has been impeded, potentially due to a lack of knowledge, active bitcoin platforms, and support. In this paper, I conduct a survey to understand factors that affect households' adoption of bitcoin. In particular, I focus on factors that capture the potential benefit and cost of adopting bitcoin. Through a public survey, participants are asked a series of questions on their willingness to adopt bitcoin. I found significant results stating that subjects were more inclined toward bitcoin contingent upon the number of platforms accepting it, the number of acquaintances using bitcoin, and the degree of personal knowledge participants have about bitcoin. These findings suggest that perceived benefit captured by network effect and convenience of use, as well as the potential cost captured by uncertainty help shape the adoption of bitcoin.
ContributorsMorrissey, Michael Joshua (Author) / Wang, Jessie (Thesis director) / Ray, Colter (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12