Zamuda A, Crescimanna V, Burguillo JC, Matos Dias J, Wegrzyn-Wolska K, Rached I, González-Vélez H, Senkerik R, Pop C, Cioara T, Salomie I & Bracciali A (2019) Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era. In: Kołodziej J & González-Vélez H (eds.) High-Performance Modelling and Simulation for Big Data Applications. Lecture Notes in Computer Science, 11400. ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet), Vilnius, Lithuania, 28.03.2019-29.03.2019. Cham, Switzerland: Springer, pp. 325-349. https://doi.org/10.1007/978-3-030-16272-6_12
Abstract This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures.