The Impact of News-Based Sentiment Analysis on Bitcoin Price Prediction: A Comparison with Machine Learning Approaches

Öz

Cryptocurrency markets differ from traditional financial instruments due to their high volatility and unpredictable nature, making price prediction studies an important area of academic research. Bitcoin is the most important cryptocurrency in terms of market value and trading volume, and it is accepted that its price movements are driven not only by technical indicators but also by investor sentiment and behavioral factors. In this context, investigating the role of sentiment analysis-based indicators in price prediction processes could contribute to both literature and practical applications. In this study, the impact of sentiment analysis derived from news content on Bitcoin price prediction was examined. Four different machine learning algorithms—Linear Regression, Rastgele Orman, Support Vector Regression, and XGBoost Regressor—were used, and the models were tested under two different scenarios created with technical data and the integration of sentiment scores. The findings revealed that ensemble-based methods, in particular, benefited significantly from sentimental data. Before improvement, the Support Vector Regression model produced the lowest error values with 5.024 MAE and 7.257 RMSE, while the XGBoost model showed weaker performance with 6.109 MAE and 7.986 RMSE. After integrating sentiment analysis, the error values decreased to 490 MAE and 772 RMSE in the Rastgele Orman model and 676 MAE and 1,137 RMSE in the XGBoost model. In contrast, the Linear Regression model showed an overfitting tendency with 0.00 error values, while Support Vector Regression provided limited improvement with 4.810 MAE and 6.960 RMSE. Overall, the findings suggest that considering news-based sentiment signals in price prediction models can increase predictive power. In this regard, the study contributes to a more comprehensive understanding of cryptocurrency markets through a behavioral finance approach.

Anahtar Kelimeler

• Cryptocurrency
• Natural language processing
• Sentiment analysis
• Machine learning
• Time series.

Haber Tabanlı Duygu Analizinin Bitcoin Fiyat Tahminine Etkisi: Makine Öğrenmesi Yaklaşımlarıyla Bir Karşılaştırma

Öz

Kripto para piyasaları, yüksek volatilite ve öngörülemez yapıları nedeniyle geleneksel finansal araçlardan ayrışmakta ve bu durum fiyat tahmini çalışmalarını akademik açıdan önemli bir araştırma alanı haline getirmektedir. Bitcoin, piyasa değeri ve işlem hacmi bakımından öne çıkan en önemli kripto varlık olup, fiyat hareketlerinin yalnızca teknik göstergelerle değil, aynı zamanda yatırımcı duyarlılığı ve davranışsal faktörlerle de yönlendirildiği kabul edilmektedir. Bu bağlamda, fiyat tahmini süreçlerinde duygu analizi tabanlı göstergelerin rolünü araştırmak hem literatüre hem de pratik uygulamalara katkı sunabilecek niteliktedir. Bu çalışmada, haber içeriklerinden elde edilen duygu analizlerinin Bitcoin fiyat tahminine etkisi incelenmiştir. Lineer Regresyon, Rastgele Orman, Destek Vektör Regresyonu ve XGBoost Regressor olmak üzere dört farklı makine öğrenimi algoritması kullanılmış, modeller teknik verilerle ve duygu skorlarının entegrasyonu ile oluşturulan iki farklı senaryo altında test edilmiştir. Bulgular, özellikle topluluk yöntemleri (ensemble) tabanlı yöntemlerin duygu verilerinden anlamlı ölçüde faydalandığını ortaya koymuştur. İyileştirme öncesinde Destek Vektör Regresyon modeli 5.024 MAE ve 7.257 RMSE ile en düşük hata değerini üretirken, XGBoost modeli 6.109 MAE ve 7.986 RMSE ile daha zayıf performans sergilemiştir. Duygu analizi entegrasyonu sonrasında ise Rastgele Orman modelinde hata değerleri 490 MAE ve 772 RMSE’ye, XGBoost modelinde ise 676 MAE ve 1.137 RMSE’ye kadar düşmüştür. Buna karşılık, Lineer Regresyon modeli 0.00 hata değerleriyle aşırı öğrenme eğilimi göstermiş, Destek Vektör Regresyon ise 4.810 MAE ve 6.960 RMSE ile sınırlı iyileşme sağlamıştır.

Anahtar Kelimeler

• Kripto para
• Doğal dil işleme
• Duygu analizi
• Makine öğrenmesi
• Zaman serisi.

Kaynakça

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