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Cryptocurrency Price Prediction using Twitter Sentiment Analysis

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Listed:
  • Haritha GB
  • Sahana N. B

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%.

Suggested Citation

  • Haritha GB & Sahana N. B, 2023. "Cryptocurrency Price Prediction using Twitter Sentiment Analysis," Papers 2303.09397, arXiv.org.
    • Handle: RePEc:arx:papers:2303.09397
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    References listed on IDEAS

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    1. Dirk G. Baur & Thomas Dimpfl, 2021. "The volatility of Bitcoin and its role as a medium of exchange and a store of value," Empirical Economics, Springer, vol. 61(5), pages 2663-2683, November.
    2. Bakas, Dimitrios & Magkonis, Georgios & Oh, Eun Young, 2022. "What drives volatility in Bitcoin market?," Finance Research Letters, Elsevier, vol. 50(C).
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