Cryptocurrency Price Prediction Using News and Social Media Sentiment
نویسندگان
چکیده
This project analyzes the ability of news and social media data to predict price fluctuations for three cryptocurrencies: bitcoin, litecoin and ethereum. Traditional supervised learning algorithms were utilized for text-based sentiment classification, but with a twist. Daily news and social media data was labeled based on actual price changes one day in the future for each coin, rather than on positive or negative sentiment. By taking this approach, the model is able to directly predict price fluctuations instead of needing to first predict sentiment. The final version of the model was able to correctly predict, on average, the days with the largest percent increases and percent decreases in price for bitcoin and ethereum over the 67 days encompassing the test set.
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