The cryptocurrency ecosystem has been the centre of discussion on many social
media platforms, following its noted volatility and varied opinions. Twitter is
rapidly being utilised as a news source and a medium for bitcoin discussion.
Our algorithm seeks to use historical prices and sentiment of tweets to
forecast the price of Bitcoin. In this study, we develop an end-to-end model
that can forecast the sentiment of a set of tweets (using a Bidirectional
Encoder Representations from Transformers - based Neural Network Model) and
forecast the price of Bitcoin (using Gated Recurrent Unit) using the predicted
sentiment and other metrics like historical cryptocurrency price data, tweet
volume, a user's following, and whether or not a user is verified. The
sentiment prediction gave a Mean Absolute Percentage Error of 9.45%, an average
of real-time data, and test data. The mean absolute percent error for the price
prediction was 3.6%.