Predicting Bitcoin Prices Using Sentiment Analysis Results

Abstract

Sentiment Analysis is a technique to determine the tone of a statement using computer software. It is an appliance of a linguistic and computer science intersection that could make a great impact on the business field. Lately, Sentiment Analysis has been used on social media platforms such as Twitter or Facebook to observe the tone of a statement. Examining the tone of tweets using the computer could be time-efficient and precise. SA studies could also be linked to Bitcoin. In this paper, I am using SA results of tweets on a given day to predict changes in the Bitcoin price and its returns. First, I collected the data, which included 31 data points of average sentiment scores and the corresponding 31 Bitcoin prices on the same day. The average sentiment scores were evaluated by VADER from a scale of -1 to 1 (-1 being the statements with the most negative tone, and 1 with the most positive). Then, I used linear regressions to predict Bitcoin price and returns using sentiment scores on the previous day/days. Predicting returns based on sentiments could allow me to find the relationship between Twitter users and Bitcoin and help me better understand the potential challenges. In the end, the predicted price was positively correlated to the sentiment scores the day before. Interestingly, the predicted return was negatively correlated with the sentiment scores and showed less correlation with a M coefficient of -0.86125.