Predicting Bitcoin Market Save

Predict bitcoin values using social sentiments (e.g. news and reddit posts)

Project README

Predictive analysis of Bitcoin price considering social sentiments.

ABSTRACT

We report on the use of sentiment analysis on news and social media to analyze and predict the price of Bitcoin. Bitcoin is the leading cryptocurrency and has the highest market capitalization among digital currencies. Predicting Bitcoin values may help understand and predict potential market movement and future growth of the technology. Unlike (mostly) repeating phenomena like weather, cryptocurrency values do not follow a repeating pattern and mere past value of Bitcoin does not reveal any secret of future Bitcoin value. Humans follow general sentiments and technical analysis to invest in the market. Hence considering people’s sentiment can give a good degree of prediction. We focus on using social sentiment as a feature to predict future Bitcoin value, and in particular, consider Google News and Reddit posts. We find that social sentiment gives a good estimate of how future Bitcoin values may move. We achieve the lowest test RMSE of 434.87 using an LSTM that takes as inputs the historical price of various cryptocurrencies, the sentiment of news articles and the sentiment of Reddit posts.

KEYWORDS

Bitcoin · Bitcoin price prediction · Cryptocurrency · Blockchain · Machine learning · Artificial intelligence · Long short-term memory (LSTM) · Gated recurrent unit (GRU) · Convolution neural network (CNN) · Sentiment analysis.

Introduction

Bitcoin has sparked a gigantic interest in cryptocurrency and blockchain technology. Since the inception of Bitcoin, cryptocurrency has gained the trust of the general population. Bitcoin has achieved the highest market capitalization among all of the cryptocurrencies. As of this writing Bitcoin market capitalization is more than 134 billion US dollars. Bitcoin gains this market value as there a huge demand for this cryptocurrency. The demand for the cryptocurrency directly translates into people’s trust in the Bitcoin and the underneath technology. Since people’s trust is involved in the rise of the cryptocurrency market, the sentiment of the general population does make a huge impact on the future of the cryptocurrency market capitalization. Hence, we use the sentiment of people in an attempt to predict future Bitcoin prices. https://news.google.com (Google News) is a nice source for collecting news posted by various journalists around the globe. Google News also provides the capability to search the news based on selected keywords and its search tools also have a feature of crawling the news based on the date of the news release. While Google News gives opinions of the various journalists we also focus on sentiments of the general population. https://www.Reddit.com (Reddit) is also one of the most famous social platforms where people can post anonymously. We also consider the sentiment of messages posted on Reddit to predict the Bitcoin price movement. Along with sentiments, we have included historical price and volume of Litecoin and Ethereum. We have trained various machine learning models to learn about the correlation between all these features and results are analyzed

Overall Goal

Results for best performing model (Expriment# 4)

Note

Please cite my paper https://arxiv.org/abs/2001.10343 if you want to use the code posted in this repository or any code referenced by this code-base and authored by me.

How to cite!

@misc{prajapati2020predictive,
    title={Predictive analysis of Bitcoin price considering social sentiments},
    author={Pratikkumar Prajapati},
    year={2020},
    eprint={2001.10343},
    archivePrefix={arXiv},
    primaryClass={cs.IR}
}

Other dependent repositories to generate dataset via web-crawling

  1. https://github.com/pratikpv/reddit_scraper_and_sentiment_analyzer
  2. https://github.com/pratikpv/google_news_scraper_and_sentiment_analyzer
  3. https://github.com/pratikpv/cryptocurrency_data_downloader
Open Source Agenda is not affiliated with "Predicting Bitcoin Market" Project. README Source: pratikpv/predicting_bitcoin_market

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