Research Article Open Access

Sentiment Identification on Tweets to Forecast Cryptocurrency’s Volatility

Rafael Calixto Ferreira de Araújo1, Alex Sandro Roschildt Pinto2 and Mauri Ferrandin3
  • 1 Department of Computer Science, Universidade Federal de Santa Catarina, Brazil
  • 2 Department of Informatics and Statistics, Federal University of Santa Catarina, Brazil
  • 3 Department of Control Engineering and Automation, Federal University of Santa Catarina, Brazil

Abstract

Cryptocurrencies have had a huge presence on social media since their creation. In current days, the constant increase of the mass of data produced by this environment has attracted several researchers to try to identify patterns with the potential to allow identification of the volatility in the crypto market before it happens. This approach involves the concept of the wisdom of the crowds, a popular theory in the economy field that in the current days may have the perfect tools to prove itself true. This scenario creates an opportunity to unite two new technologies, social media, and cryptocurrencies to the newest Natural Language Processing (NLP) tools, and produces a study in a rich and unexplored field. Executing a detailed sentiment analysis, this study intents to analyze the forecast of the volatility of cryptocurrencies through the detection and evaluation of several categories of sentiments on messages on twitter when it is associated with a specific cryptocurrency. To achieve this, an NLP model was trained with the GoEmotions dataset to identify and categorize emotions, and results were used to calculate the forecast of the cryptocurrency. Index terms cryptocurrency, social media, Natural Language Processing (NLP), GoEmotions.

Journal of Computer Science
Volume 19 No. 5, 2023, 619-628

DOI: https://doi.org/10.3844/jcssp.2023.619.628

Submitted On: 6 October 2022 Published On: 3 May 2023

How to Cite: de Araújo, R. C. F., Pinto, A. S. R. & Ferrandin, M. (2023). Sentiment Identification on Tweets to Forecast Cryptocurrency’s Volatility. Journal of Computer Science, 19(5), 619-628. https://doi.org/10.3844/jcssp.2023.619.628

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Keywords

  • Cryptocurrency
  • Social Media
  • Natural Language Processing
  • NLP
  • GoEmotions