Tweet sentiment analysis for cryptocurrencies
Yükleniyor...
Dosyalar
Tarih
2021-10-13
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Many 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.
Açıklama
Anahtar Kelimeler
BERT, Cryptocurrencies, Random forest algorithm, Sentiment analysis, Classification (of information), Costs, Electronic money, Random forests, Social networking (online), Cryptocurrency, Data cleaning, Data collection, Hashtags, Labeled data, Positive/negative, Random forest classifier, Decision trees
Kaynak
2021 6th International Conference on Computer Science and Engineering (UBMK)
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Şaşmaz, E. & Tek, F. B. (2021). Tweet sentiment analysis for cryptocurrencies. 2021 6th International Conference on Computer Science and Engineering (UBMK), 613-618. doi:10.1109/UBMK52708.2021.9558914
