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Table 6 Comparison with other studies

From: Bitcoin price change and trend prediction through twitter sentiment and data volume

Study Maximum accuracy obtained Days in dataset
Present (Direction-BiLSTM) 64.2% 450\(\sim\)
Present (Voting Classifier) 77.2% 450\(\sim\)
Predicting the Price of Bitcoin Using Machine Learning-2018 McNally et al. 52.78% 1065\(\sim\)
An Advanced CNN-LSTM Model for Cryptocurrency Forecasting-2021 Livieris et al. 55.03% 1400\(\sim\)
Bitcoin price forecasting method based on CNN-LSTM hybrid neural network model-2019 Yan Li, Wei Dai 64% (Precision) 600\(\sim\)
Bitcoin Response to Twitter Sentiments Galenchuk et al. 68.6% 912\(\sim\)
Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning-2019 Valencia et al. 72% 60\(\sim\)
Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis-2018 Pant et al. 77.62% 180\(\sim\)
Predicting Bitcoin price fluctuation with Twitter sentiment analysis-2017 Stenqvist, Lönnö 83% 30\(\sim\)
Sentiment Analysis Based Direction Prediction in Bitcoin using Deep Learning Algorithms and Word Embedding Models-2020 Kilimci 89.13% 90\(\sim\)