Bitcoin Duygu Analizinde Duygu Tanıma ve Sınıflandırma için Makine Öğrenmesi Yaklaşımı

Yıl 2024, Cilt: 29 Sayı: 3, 913 - 926, 31.12.2024

Erol Kına , Emre Biçek

https://doi.org/10.53433/yyufbed.1532649

Öz

Bitcoin en yüksek piyasa değerine sahip kripto para birimidir ve diğer para birimlerine kıyasla hızlı ve değişken fiyat dalgalanmalarıyla bilinir. Bu durum Bitcoin’in fiyat tahmini için fırsatlar sunmakta ve araştırmacıların ilgisini çekmektedir. Twitter (X), en yaygın kullanılan sosyal medya platformlarından biridir. Bu çalışma kapsamında, makine öğrenimi algoritmalarını kullanarak Bitcoin ile ilgili X yorumlarının duyarlılığı analiz edilmiştir. Bitcoin'e yönelik kullanıcı duyarlılığını sınıflandırmak için spesifik makine öğrenimi teknikleri kullanılmış ve metni sayısal vektörler olarak ifade etmek için standart kelime torbası ve terim frekansı-ters belge frekansı (TF-IDF) yöntemleri makine öğrenimi yaklaşımlarıyla karşılaştırılmıştır. Son olarak, kripto para birimlerinin gelişiminde her duygunun önemini belirlemek için anahtar kelime sıralaması yapılarak, metin tabanlı verilerin temsilini kolaylaştıran Bag-of-words ve TF-IDF yöntemleri kullanılmıştır. En iyi sonuç TF-IDF yöntemi kullanılarak karar ağaçları algoritmasıyla (%98.74 doğruluk) elde edilmiş, çalışmada Bag-of-words yönteminin genel olarak daha iyi sonuçlar ürettiği görülmüştür.

Anahtar Kelimeler

Algoritmalar, Bitcoin, DDİ, Duygu analizi, Makine öğrenmesi

Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis

Öz

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.

Anahtar Kelimeler

Algorithms, Bitcoin, Machine learning, NLP, Sentiment analysis

Kaynakça

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