Duygu Analizi ile Kripto Para İşlemlerindeki Dalgalanmaların İncelenmesi

Year 2024, EARLY VIEW, 1 - 1

Uğur Demirel , Handan Çam

https://doi.org/10.2339/politeknik.1518826

Abstract

Bu çalışma, sosyal medya platformlarında ve çevrimiçi forumlarda paylaşılan yorumları kullanarak, Türkiye'deki kripto borsalarında listelenen popüler kripto para birimleri hakkındaki kamuoyunun duyarlılığını araştırmaktadır. Araştırma, Türkçe metinlere odaklanan duygu analizi çalışmalarının eksikliklerini gidererek mevcut bilgi birikimini artırmayı amaçlamaktadır. Sosyal medya ve çevrimiçi forumlardan toplanan veriler duygu analizi teknikleriyle incelenmiştir. Toplam 607.592 yorum analiz edildi ve bunların 89.986'sı olumsuz, 72.655'i olumlu ve 444.951'i nötr olarak sınıflandırılmıştır. İkili sınıflandırma için 89.986 negatif ve 72.655 pozitif örnek seçildi ve makine öğrenimi modelleri eğitildi ve 162.641 örnek üzerinde test edildi. Çalışmanın metodolojisi, makine öğrenimi sınıflandırıcıları kullanılarak elde edilen duygu analizi sonuçlarının derinlemesine incelenmesini içermektedir. Bulgular, çeşitli kripto para birimlerinin farklı sosyal medya platformlarında nasıl algılandığını göstermektedir. Örneğin, BTC (Bitcoin) Investing.com ve Telegram'da genel olarak olumsuz algılanırken, ETH (Ethereum) genellikle daha olumsuz görüşler sergilemektedir. Bu sonuçlar, yatırımcıların kripto para birimlerine yönelik algılarını ve piyasa beklentilerini anlamalarına yardımcı olmaktadır. Bu çalışma, sosyal medya duyarlılık analizinin kripto para piyasalarındaki rolünü derinleştirerek gelecekteki araştırmalar için yeni yöntem ve yaklaşımların geliştirilmesine katkıda bulunmaktadır.

Keywords

Kripto para, Metin madenciliği, Doğal dil işleme, Makine öğrenmesi, Duygu analizi

Investigation of Fluctuations in Cryptocurrency Transactions with Sentiment Analysis

Abstract

This study investigates public sentiment about popular cryptocurrencies listed on crypto exchanges in Turkey, using comments shared on social media platforms and online forums. The research seeks to enhance the existing body of knowledge by overcoming the shortcomings of sentiment analysis studies focused on Turkish texts. Data collected from social media and online forums were examined with sentiment analysis techniques. A total of 607,592 comments were analyzed, of which 89,986 were classified as negative, 72,655 as positive, and 444,951 as neutral. For binary classification, 89,986 negative and 72,655 positive examples were selected and machine-learning models were trained and tested on 162,641 examples. The study's methodology includes an in-depth examination of sentiment analysis results obtained using machine learning classifiers. The findings show how various cryptocurrencies are perceived on different social media platforms. For instance, BTC (Bitcoin) is generally perceived negatively on Investing.com and Telegram, while ETH (Ethereum) generally displays more negative views. These results help investors understand their perceptions and market expectations towards cryptocurrencies. This study deepens the role of social media sentiment analysis in cryptocurrency markets, contributing to the development of new methods and approaches for future research.

Keywords

Text mining, Natural language processing, Machine learning, Sentiment analysis, Cryptocurrency

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