Predicting Bitcoin price fluctuation with Twitter sentiment analysis

Abstract

Programmatically deriving sentiment has been the topic of many a thesis: it’s application in analyzing 140 character sentences, to that of 400-word Hemingway sentences; the methods ranging from naive rule based checks, to deeply layered neural networks. Unsurprisingly, sentiment analysis has been used to gain useful insight across industries, most notably in digital marketing and financial analysis. An advancement seemingly more excitable to the mainstream, Bitcoin, has risen in number of Google searches by three-folds since the beginning of this year alone, not unlike it’s exchange rate. The decentralized cryptocurrency, arguably, by design, a pure free market commodity – and as such, public perception bears the weight in Bitcoins monetary valuation. This thesis looks toward these public perceptions, by analyzing 2.27 million Bitcoin-related tweets for sentiment fluctuations that could indicate a price change in the near future. This is done by a naive method of solely attributing rise or fall based on the severity of aggregated Twitter sentiment change over periods ranging between 5 minutes and 4 hours, and then shifting these predictions forward in time 1, 2, 3 or 4 time periods to indicate the corresponding BTC interval time. The prediction model evaluation showed that aggregating tweet sentiments over a 30 min period with 4 shifts forward, and a sentiment change threshold of 2.2%, yielded a 79% accuracy.