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Predicting Cryptocurrency Prices During Periods of Conflict: A Comparative Sentiment Analysis Using SVM, CNN-LSTM, and Pysentimento

Author

Listed:
  • Muhammad Nabil Rateb

    (Alexandria University)

  • Sameh Alansary

    (Alexandria University)

  • Marwa Khamis Elzouka

    (Alexandria University)

  • Mohamad Galal

    (Nile University, 26th of July Corridor Sheikh Zayed City)

Abstract

In this paper, we propose a method for predicting the prices and trends of cryptocurrencies using sentiment analysis and time series forecasting. In this study, more than one million tweets spanning 3 months (March, June, and December 2022) regarding three cryptocurrencies, Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) during the Russian-Ukrainian War, are considered. Two models, a convolutional neural network with long short-term memory (CNN-LSTM) and a support vector machine (SVM) with GloVe and TF-IDF features, are trained on a labeled dataset of more than fifty thousand tweets about Bitcoin labeled as positive, negative, and neutral. A pretrained model (Pysentimento) for sentiment analysis is also employed to compare the performances of the three models. The models are tested on the labeled dataset and then evaluated on the unlabeled tweets, revealing that Pysentimento’s level of accuracy outperforms the other two models. Google Trends, along with the opening and closing prices, and the volume of the three cryptocurrencies, in addition to the results of Pysentimento sentiment classification, are employed to apply the Pearson correlation coefficient and conduct price prediction analysis using the SARIMA model. It is found that Bitcoin may appeal to those seeking stability and a known record of accomplishment, while Binance Coin and Ethereum may attract investors looking for more diverse opportunities. A data-centric approach that can provide valuable insights and predictions for the cryptocurrency market, especially in the context of the Russian-Ukrainian War, which poses significant challenges and uncertainties for investors and traders, is demonstrated.

Suggested Citation

  • Muhammad Nabil Rateb & Sameh Alansary & Marwa Khamis Elzouka & Mohamad Galal, 2024. "Predicting Cryptocurrency Prices During Periods of Conflict: A Comparative Sentiment Analysis Using SVM, CNN-LSTM, and Pysentimento," SN Operations Research Forum, Springer, vol. 5(3), pages 1-40, September.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:3:d:10.1007_s43069-024-00352-6
    DOI: 10.1007/s43069-024-00352-6
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    References listed on IDEAS

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    1. N. L. Balasudarsun & Bikramaditya Ghosh & Sathish Mahendran, 2022. "Impact of Negative Tweets on Diverse Assets during Stressful Events: An Investigation through Time-Varying Connectedness," JRFM, MDPI, vol. 15(6), pages 1-12, June.
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