HyVADRF: Hybrid VADER-Random Forest and GWO for Bitcoin Tweet Sentiment Analysis

Abstract

In recent years, Bitcoin and other cryptocurrencies have been increasingly considered investment options for emerging markets. However, Bitcoin's erratic behavior has discouraged some potential investors. To get insights into its behavior and price fluctuation, past studies have discovered the correlation between Twitter sentiments and Bitcoin behavior. Most of them have exclusively focused on their relationships, instead of the Twitter sentiment analysis itself. Finding the most suitable classification algorithms for sentiment analysis for this kind of data is challenging. For the enormous data in Twitter, the supervised sentiment analysis approach of unlabeled data can be time-consuming and expensive, which has been studied to be superior to unsupervised ones. As such, we propose the HyVADRF (hybrid valence aware dictionary and sentiment reasoner (VADER)-random forest) and gray wolf optimizer (GWO) model. A semantic and rule-based VADER was used to calculate polarity scores and classify sentiments, which overcame the weakness of manual labeling, while a random forest was utilized as its supervised classifier. Furthermore, considering Twitter's massive size, we collected over 3.6 million tweets and analyzed various dataset sizes as these are related to the model's learning process. Lastly, GWO parameter tuning was conducted to optimize the classifier's performance. The results show that 1) the HyVADRF model had an accuracy of 75.29%, precision of 70.22%, recall of 87.70%, and F1-score of 78%. 2) The most ideal dataset size percentage is 90% of the total collected tweets ( n =1 ,249,060). 3) The standard deviations are 0.0008 for accuracy and F1-score and 0.0011 for precision and recall. Hence, the HyVADRF model consistently delivers stable results.