Cryptocurrency Price Prediction using Twitter Sentiment Analysis

• URL: http://arxiv.org/abs/2303.09397v1
• Date: Fri, 3 Mar 2023 18:42:01 GMT
• Title: Cryptocurrency Price Prediction using Twitter Sentiment Analysis
• Authors: Haritha GB and Sahana N.B
• Abstract summary: This study seeks to use historical prices and sentiment of tweets to forecast the price of Bitcoin. We develop an end-to-end model that can forecast the sentiment of a set of tweets and forecast the price of Bitcoin. The sentiment prediction gave a Mean Absolute Percentage Error of 9.45%, an average of real-time data, and test data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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%.

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