Sentiment Analysis on Cryptocurrency Tweets Using Machine Learning

Abstract

Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Many investors believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of sentiment analysis and emotion for cryptocurrencies. For the study, we targeted (BTC) Bitcoin and collected related data. The data collection, cleaning were essential components of the study. Analysis of the sentiments about cryptocurrency is highly desirable to provide a holistic view of peoples' perceptions. In this regard, this study performs both sentiment analysis and emotion detection using the tweets related to the Bitcoin which are widely used for predicting the market prices of cryptocurrency. For increasing the efficacy of the analysis, a deep learning ensemble model LSTM-GRU is proposed that combines two recurrent neural networks applications. Comparatively, a larger number of people feel happy with the use of cryptocurrency, followed by fear and surprise emotions. The model achieves the highest performance for sentiment analysis with a 0.91 accuracy score and the highest emotion 0.83. Similarly, LSTM-GRU outperforms all other models in terms of correct and wrong predictions for both sentiment analysis 0.99 and emotion detection 0.98. Key Words: bitcoin, sentiment analysis, machine learning, cryptocurrencies, tweets