# Cryptocurrency Price Prediction using Twitter Sentiment Analysis

**Authors:** Haritha GB, Sahana N. B  
**arXiv:** [2303.09397](https://arxiv.org/abs/2303.09397)  
**Date:** 2023-03-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2303.09397v1/

## Abstract

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%.