Tweet Sentiment Analysis for Cryptocurrencies


Şaşmaz E., Tek F. B.

6th International Conference on Computer Science and Engineering, UBMK 2021, Ankara, Turkey, 15 - 17 September 2021, pp.613-618 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ubmk52708.2021.9558914
  • City: Ankara
  • Country: Turkey
  • Page Numbers: pp.613-618
  • Keywords: BERT, Cryptocurrencies, Random forest algorithm, Sentiment analysis
  • Istanbul Technical University Affiliated: No

Abstract

© 2021 IEEEMany traders believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of automated sentiment analysis for cryptocurrencies. For the study, we targeted one cryptocurrency (NEO) altcoin and collected related data. The data collection and cleaning were essential components of the study. First, the last five years of daily tweets with NEO hashtags were obtained from Twitter. The collected tweets were then filtered to contain or mention only NEO. We manually tagged a subset of the tweets with positive, negative, and neutral sentiment labels. We trained and tested a Random Forest classifier on the labeled data where the test set accuracy reached 77%. In the second phase of the study, we investigated whether the daily sentiment of the tweets was correlated with the NEO price. We found positive correlations between the number of tweets and the daily prices, and between the prices of different crypto coins. We share the data publicly.