Version 1
: Received: 8 September 2020 / Approved: 10 September 2020 / Online: 10 September 2020 (04:02:53 CEST)
How to cite:
Verma, M.; Sharma, P. Money Often Costs Too Much: A Study to Investigate The Effect Of Twitter Sentiment On Bitcoin Price Fluctuation. Preprints2020, 2020090216. https://doi.org/10.20944/preprints202009.0216.v1
Verma, M.; Sharma, P. Money Often Costs Too Much: A Study to Investigate The Effect Of Twitter Sentiment On Bitcoin Price Fluctuation. Preprints 2020, 2020090216. https://doi.org/10.20944/preprints202009.0216.v1
Verma, M.; Sharma, P. Money Often Costs Too Much: A Study to Investigate The Effect Of Twitter Sentiment On Bitcoin Price Fluctuation. Preprints2020, 2020090216. https://doi.org/10.20944/preprints202009.0216.v1
APA Style
Verma, M., & Sharma, P. (2020). Money Often Costs Too Much: A Study to Investigate The Effect Of Twitter Sentiment On Bitcoin Price Fluctuation. Preprints. https://doi.org/10.20944/preprints202009.0216.v1
Chicago/Turabian Style
Verma, M. and Pritish Sharma. 2020 "Money Often Costs Too Much: A Study to Investigate The Effect Of Twitter Sentiment On Bitcoin Price Fluctuation" Preprints. https://doi.org/10.20944/preprints202009.0216.v1
Abstract
Introduced in 2009, Bitcoin has demonstrated a huge potential as the world’s first digital currency and has been widely used as a financial investment. Our research aims to uncover the relationship between Bitcoin prices and people’s sentiments about Bitcoin on social media. Among various social media platforms, micro-blogging is one of the most popular. Millions of people use micro-blogging platforms to exchange ideas, broadcast views, and to provide opinions on different topics related to politics, culture, science, and technology. This makes them a potentially rich source of data for sentiment analysis. Therefore we chose one of the busiest micro-blogging platforms, Twitter, to perform sentiment analysis on Bitcoin. We used ELMo embedding model to convert Bitcoin-related tweets into a vector form and SVM classifier to divide the tweets into three sentiment categories - positive, negative, and neutral. We then used the sentiment data to find its relation with Bitcoin price fluctuation using the linear mixed model.
Keywords
Bitcoin; SVM; linear mixed models; word embedding; ELMo
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
