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

Erol Kına; Emre Biçek

doi:10.53433/yyufbed.1532649

EN TR

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

Bitcoin Duygu Analizinde Duygu Tanıma ve Sınıflandırma için Makine Öğrenmesi Yaklaşımı

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

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Algoritmalar ve Hesaplama Kuramı, Veri Yapıları ve Algoritmalar

Bölüm

Araştırma Makalesi

Yazarlar

Erol Kına ^*
0000-0002-7785-646X
Türkiye

Emre Biçek
0000-0001-6061-9372
Türkiye

Yayımlanma Tarihi

31 Aralık 2024

Gönderilme Tarihi

13 Ağustos 2024

Kabul Tarihi

23 Eylül 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 29 Sayı: 3

DOI

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

IZ

https://izlik.org/JA44HG64EA

APA

Kına, E., & Biçek, E. (2024). Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 29(3), 913-926. https://doi.org/10.53433/yyufbed.1532649

AMA

1.Kına E, Biçek E. Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis. YYUFBED. 2024;29(3):913-926. doi:10.53433/yyufbed.1532649

Chicago

Kına, Erol, ve Emre Biçek. 2024. “Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 29 (3): 913-26. https://doi.org/10.53433/yyufbed.1532649.

EndNote

Kına E, Biçek E (01 Aralık 2024) Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 29 3 913–926.

IEEE

[1]E. Kına ve E. Biçek, “Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis”, YYUFBED, c. 29, sy 3, ss. 913–926, Ara. 2024, doi: 10.53433/yyufbed.1532649.

ISNAD

Kına, Erol - Biçek, Emre. “Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 29/3 (01 Aralık 2024): 913-926. https://doi.org/10.53433/yyufbed.1532649.

JAMA

1.Kına E, Biçek E. Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis. YYUFBED. 2024;29:913–926.

MLA

Kına, Erol, ve Emre Biçek. “Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 29, sy 3, Aralık 2024, ss. 913-26, doi:10.53433/yyufbed.1532649.

Vancouver

1.Erol Kına, Emre Biçek. Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis. YYUFBED. 01 Aralık 2024;29(3):913-26. doi:10.53433/yyufbed.1532649

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

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

Öz

Anahtar Kelimeler

Bitcoin Duygu Analizinde Duygu Tanıma ve Sınıflandırma için Makine Öğrenmesi Yaklaşımı

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

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