Social Sentiment Analysis for Prediction of Cryptocurrency Prices Using Neuro-Fuzzy Techniques


BİRİM Ş., SÖNMEZ F. E.

4th International Conference on Intelligent and Fuzzy Systems (INFUS), Bornova, Turkey, 19 - 21 July 2022, vol.505, pp.606-616 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 505
  • Doi Number: 10.1007/978-3-031-09176-6_68
  • City: Bornova
  • Country: Turkey
  • Page Numbers: pp.606-616
  • Keywords: Neuro-fuzzy network, ANFIS, Cryptocurrencies, PERFORMANCE
  • Istanbul Technical University Affiliated: No

Abstract

This study aims to provide an intelligent system that uses neuro-fuzzy techniques to predict daily prices of selected cryptocurrencies using a combination of twitter sentiment and google trend data. Although previously used to predict bitcoin prices, Neuro-fuzzy systems are used with this study for the first time with sentiment analysis to predict price trends of digital currencies. An adaptive neuro-fuzzy interface-based network was used to predict the prices of three selected cryptocurrencies, Ethereum, Ripple and Litecoin. The difference from the current study is that Twitter sentiment and Google trends have not been used as a predictor in a neuro-fuzzy network before. ANFIS has the advantage of combining the properties of fuzzy systems and neural networks. This advantage is manifested in producing lower error and higher accuracy in predictions. According to the findings, different results were obtained for different cryptocurrencies in the model in which the ANFIS estimation method was used. For ETH and LTC, the best forecast performance is obtained when twitter sentiment and google trends are used together. The Twitter sentiment model took second place by only a small margin. For XRP, only twitter sentiment shows the best forecast performance.