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- W4382118639 abstract "Media news constantly changes the sentiment of traders and investors in the cryptocurrency market and impacts their decisions. Data collected from various news aggregators aid in the early indication of the cryptocurrency market movement. The sentiment and public mood states can be uncovered using different techniques such as data mining, natural language processing, and opinion mining. Bitcoin plays the most dominant role in the global economy, and it is extremely important to understand the correlation between news sentiment and price volatility of Bitcoin. This paper assesses news collections and interprets them to forecast the market movement of Bitcoin. Sentiment analysis and deep learning methods, namely long short-term memory (LSTM), gated recurrent units (GRUs), bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent units (BiGRUs), were used for prediction modeling of Bitcoin prices. Each model was considered on its ability to forecast the Bitcoin early market movement using news sentiment in the Covid-19 era. BiGRU prediction model is developed which achieved an overall accuracy of 70% and showed better performance than the state-of-art cryptocurrency price prediction models based on sentiment analysis." @default.
- W4382118639 created "2023-06-27" @default.
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- W4382118639 date "2023-01-01" @default.
- W4382118639 modified "2023-09-24" @default.
- W4382118639 title "Bidirectional Gated Recurrent Unit (BiGRU)-Based Bitcoin Price Prediction by News Sentiment Analysis" @default.
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- W4382118639 doi "https://doi.org/10.1007/978-981-19-8669-7_4" @default.
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