Cryptocurrency Trend Prediction Through Hybrid Deep Transfer Learning

Abstract

The impact of sentiment analysis of comments on social networks such as X (Twitter) on the cryptocurrency market’s behavior has been proven. Also, traditional sentiment analysis and not considering the possible aspects of tweets can cause the deep model to be misleading in predicting the price trend of cryptocurrencies. In this research, a model using transfer learning and the combination of pretrained DistilBERT networks, BiGRU deep neural network, and attention layer is presented to analyze the sentiments based on the aspect of tweets and predict the price trend of eight cryptocurrencies. These tweets are the opinions of 70 cryptocurrency expert influencers. After preprocessing, these tweets are injected into the hybrid model of DistilBERT, BiGRU, and attention layer (HDBA) to extract the aspect and determine the polarity of each aspect. The output of the HDBA model is entered into the combined model of BiGRU and the attention layer (HBA) to predict the price trend of each cryptocurrency in intervals of 1–10 days. The output of the HBA model is the best time interval of the influence of the sentiments of tweets on the price trend of cryptocurrencies. The results show that the HDBA model has improved the performance of the aspect-based sentiment analysis task by an average of 3% in the benchmark datasets. The results of the HBA model also show that this model has been able to predict the best time frame of the impact of sentiments on the behavior of the cryptocurrency market with an average accuracy of 68% and a precision of 73%.