Twitter Sentiment Analysis-Based Adjustment of Cryptocurrency Action Recommendation Model for Profit Maximization

【Author】 Park, Jaehyun; Seo, Yeong-Seok

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】Cryptocurrencies have recently attracted considerable attention, resulting in research mainly on deep learning-based price prediction models to maximize profit. Two research approaches have been adopted. Studies adopting the first approach directly predict the future cryptocurrency price. Long short-term memory (LSTM) and gated recurrent unit (GRU), which show high performance in time-series data, are mainly used for this approach. Further, studies adopting the second approach recommend actions to investors to maximize profits, such as "Sell", "Buy", and "Wait." In this approach, classification models are used and results are derived based on probabilities. However, these action recommendation models do not consider the quality of the result. For example, it is risky to accept the result when the probability that the result of the action recommendation model for two classes is the correct answer is approximately 51%. To solve this problem, we recommend a method for adjusting the result of the action recommendation model based on Twitter sentiment analysis. The experimental results show that the proposed adjustment method improves the performance by approximately 3% compared to the conventional methods and are statistically validated.

【Keywords】Cryptocurrency; Predictive models; Social networking (online); Blogs; Sentiment analysis; Analytical models; Data models; Bitcoin; Bitcoin action recommendation; deep learning; sentiment analysis; profit maximization

【发表时间】2023

【收录时间】2023-06-17

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-价格预测