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

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Intelligent and Fuzzy Systems (INFUS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 505))

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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.

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Author information

Authors and Affiliations

  1. Manisa Celal Bayar University, Salihli, 45300, Turkey

    Şule Öztürk Birim & Filiz Erataş Sönmez

Authors
  1. Şule Öztürk Birim
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  2. Filiz Erataş Sönmez
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Corresponding author

Correspondence to Şule Öztürk Birim .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Department of Industrial Engineering, Galatasaray University, Istanbul, Turkey

    A. Cagri Tolga

  3. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Sezi Cevik Onar

  4. Department of Industrial Engineering, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  5. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Basar Oztaysi

  6. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Irem Ucal Sari

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Birim, Ş.Ö., Sönmez, F.E. (2022). Social Sentiment Analysis for Prediction of Cryptocurrency Prices Using Neuro-Fuzzy Techniques. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-09176-6_68

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