Abstract:
It is noticed that sentiment of related tweets has great influence on the price trend of cryptocurrency, especially bitcoin. This research focuses on bitcoin-related twee...Show MoreMetadata
Abstract:
It is noticed that sentiment of related tweets has great influence on the price trend of cryptocurrency, especially bitcoin. This research focuses on bitcoin-related tweets and bitcoin price in January 2019 to May 2019, and employs Natural Language Process (NLP) and Machine Learning (ML) to study the relationship between them. In this work, tweets are analyzed by vaderSentiment and TextBlob respectively. The best trained model is Naive Bayes with TextBlob as sentiment analysis. It reaches 0.729 for F1 score and 0.641 for precision. This research makes a contribution to 1) comparing vaderSentiment and TextBlob on tweets sentiment analysis and 2) training models to learn relationship between bitcoin related tweets and bitcoin price trends in the following days.
Published in: ICMLCA 2021; 2nd International Conference on Machine Learning and Computer Application
Date of Conference: 17-19 December 2021
Date Added to IEEE Xplore: 17 March 2022
Print ISBN:978-3-8007-5739-8
Conference Location: Shenyang, China