Akademik Veri Yönetim Sistemi
Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, cilt.29, sa.2, ss.913-926, 2024 (Hakemli Dergi)
Bitcoin is the most valuable cryptocurrency and is renowned for its
rapid and volatile price fluctuations in comparison to other currencies.
This offers potential for the prediction of Bitcoin prices and has
attracted the interest of researchers. Twitter (X) is one of the most
widely used social media platforms. The aim of this study is to analyse
the sentiment expressed in comments about bitcoin on the social media
platform X using a variety of machine learning algorithms. A variety of
machine learning techniques are used to classify user sentiment towards
bitcoin. Moreover, the efficacy of standard bag-of-words and term
frequency-inverse document frequency (TF-IDF) methods is evaluated in
comparison with machine learning approaches for the purpose of
expressing text as numerical vectors. Finally, a keyword ranking was
performed to determine the importance of each sentiment in the
development of cryptocurrencies. The bag-of-words and TF-IDF methods
were used, which facilitate the representation of text-based data. The
best result was obtained with the decision trees algorithm (98.74%
accuracy) using the TF-IDF method. The bag-of-words method was found to
produce better results in general.