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Innovations in Computer Science and Engineering pp 47–55Cite as

Cryptocurrency Price Prediction Based on Historical Data and Social Media Sentiment Analysis

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 103))

Abstract

The cryptocurrency space is highly volatile, and predictive systems working in this space are still in their infancy phase. The findings made during an extensive literature survey suggest the lack of a balanced approach and the right combination of data sources, which lead to biased feature sets and discriminative results. These have an impact on the accuracy of the models and skew the classification and prediction results. In this paper, we explore a better approach where a combination of sentiment analysis of social media content, contemporary pricing and market volume data is considered to extract a refined feature set. The features extracted from the preprocessing pipeline will then be used to classify and predict future pricing using a neural network model.

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Acknowledgements

We express our deep sense of gratitude to the Founder President of Amity Group, Dr. Ashok K. Chauhan, for his keen interest in promoting research in Amity University and has always been an inspiration for achieving great heights.

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Authors and Affiliations

  1. Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India

    Soumyajit Pathak & Alpana Kakkar

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  1. Soumyajit Pathak
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  2. Alpana Kakkar
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Editor information

Editors and Affiliations

  1. Guru Nanak Institutions, Hyderabad, Telangana, India

    Harvinder Singh Saini

  2. Guru Nanak Institutions, Hyderabad, Telangana, India

    Rishi Sayal

  3. School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  4. Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, Telangana, India

    Govardhan Aliseri

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Pathak, S., Kakkar, A. (2020). Cryptocurrency Price Prediction Based on Historical Data and Social Media Sentiment Analysis. In: Saini, H., Sayal, R., Buyya, R., Aliseri, G. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-15-2043-3_7

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  • DOI: https://doi.org/10.1007/978-981-15-2043-3_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2042-6

  • Online ISBN: 978-981-15-2043-3

  • eBook Packages: EngineeringEngineering (R0)

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