Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data

【Author】 Loginova, Ekaterina; Tsang, Wai Kit; van Heijningen, Guus; Kerkhove, Louis-Philippe; Benoit, Dries F.

【Source】MACHINE LEARNING

【影响因子】5.414

【Abstract】The emergence of cryptocurrency markets has drastically changed how online transactions are conducted and provide a new investment opportunity. This study contributes to the literature on directional cryptocurrency price returns prediction by expanding the set of meaningful features extracted from textual data with sentiment analysis and comparing their usefulness across multiple data sources. In contrast to previous studies, we use fine-grained topic-sentiment features. More specifically, aspect-based sentiment analysis models, JST and TS-LDA, are implemented to incorporate joint topical-sentiment features and the degree of text subjectivity. We collected, and make available, a dataset, which consists of data scraped from Reddit, Bitcointalk and CryptoCompare sources, to demonstrate that proposed features lead to interpretable topics and an improvement in predictive performance.

【Keywords】Cryptocurrency; Sentiment analysis; Reddit; Financial forecasting

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【收录时间】2022-01-01

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