Evaluating Sentiment C1assifiers for Bitcoin Tweets in Price Prediction Task

Abstract

Bitcoin alongside other cryptocurrencies became one of the largest trends recently, due to its redefinition of the concept of money, and its price fluctuation. Especially on the social media, people keep discussing Bitcoin topics, consulting, and advising about cryptocurrency trading. This paper explores the relationship between Twitter feed on Bitcoin and sentiment analysis of it, comparing and evaluating different data mining classifiers and deep learning methods that might help in better sentiment classification of Bitcoin tweets, the study uses different language modeling approaches, such as tweet embedding and N-Gram modeling. We also evaluate the quality of automated sentiment classification in comparison to manually assigned sentiment labeling. The results show that the manual approach gives significantly better results in some datasets, and superior performance of MLP, WiSARD and decision tree methods. On the other hand, R-Auto Tweets Sentiment (RATS) gives more stable performance overall datasets. using time-series, we found partial correlation between Bitcoin price fluctuation and sentiment class accuracy fluctuations using different machine learning algorithms.