Forecasting cryptocurrency value by sentiment analysis: an HPC-oriented survey of the state-of-the-art in the cloud era
UNIVERSAL IDENTIFIER: http://hdl.handle.net/11093/3215
EDITED VERSION: http://link.springer.com/10.1007/978-3-030-16272-6_12
UNESCO SUBJECT: 1203.04 Inteligencia Artificial
DOCUMENT TYPE: bookPart
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.
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