Research Article
In Text Classification, Bitcoin Prices and Analysis of Expectations in Social Media with Artificial Neural Networks
Abstract
In
recent years, Web 2.0 services such as blogs, tweets, forums, emails have been
widely used as communication channel. Also, social media; it is considered to
be the easiest and most up-to-date way to both share information and express
opinions such as requests, complaints, and wishes. As in many fields, the
effect of social media on Bitcoin prices has been addressed in the last few
years.
Bitcoin
is an investment tool that has been underlined for years, and is increasing in
popularity day by day. Bitcoin, an electronic currency system that is
decentralized, states a radical change in financial systems that has attracted
many users. In this study, interaction of social media with Bitcoin price was
revealed, particularly based on tweets obtained from Twitter channel. For this
purpose, various analyses were carried out by using classification algorithms
in machine learning methods over a total of 2,819,784 tweets posted by Twitter
users between 06.10.2018-19.05.2019. When the findings were evaluated,
Artificial Neural Networks with the highest accuracy rate of 90% was used in
text classification. In addition, bilateral correlations were made with Bitcoin
prices and classified positive / negative tweet rates. The correlation
coefficient of 0.681 was found to be positively correlated with higher than
moderate strength.
References
- Alghobiri, M. (2019). Using data mining algorithm for sentiment analysis of users’ opinions about Bitcoin cryptocurrency. Journal of Theoretical and Applied Information Technology, 97(8), 2195-2205.
- Basu, A., Walters, C., & Shepherd, M. (2003). Support vector machines for text categorization. Proceedings of the 36th Annual Hawaii International Conference on System Sciences, 4(4), 1-8.
- Blanzieri, E. & Bryl, A. (2008). A survey of learning-based techniques of email spam filtering. Artificial Intelligence Review, 29(1), 63-92.
- Ceyhan, K., Kurtulmaz, E., Sert, O. C., & Özyer, T. (2018). Bitcoin movement prediction with text mining. 26th Signal Processing and Communications Applications Conference, 1-4.
- Cheuque Cerda, G. & L Reutter, J. (2019). Bitcoin price prediction through opinion mining. In Companion Proceedings of The 2019 World Wide Web Conference, 755-762.
- Colianni, S., Rosales, S. & Signorotti, M. (2015). Algorithmic trading of cryptocurrency based on Twitter sentiment analysis. Erişim Tarihi: 25.07.2019 http://cs229.stanford.edu/proj2015/029_report.pdf, ss. 1-5.
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
- Coşkun, C., & Baykal, A. (2011). Veri madenciliğinde sınıflandırma algoritmalarının bir örnek üzerinde karşılaştırılması. Akademik Bilişim, 1-8.
- Deng, X., Li, Y., Weng, J., & Zhang, J. (2019). Feature selection for text classification: A review. Multimedia Tools and Applications, 78(3), 3797-3816.
- Erdal, H. (2015). Makine öğrenmesi yöntemlerinin inşaat sektörüne katkısı: basınç dayanımı tahminlemesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 12(1), 109-114.
- Dumais S, Platt J, Heckerman D, Sahami M (1998) Inductive learning algorithms and representations for text categorization. In: Proceedings of the seventh international conference on Information and knowledge management. ACM, 148–155.
- Eriş, M. (2006). Derin öğrenme yöntemleri kullanarak adli bilişim incelemelerinde delil çıkarımının gerçekleştirilmesi. (Basılmamış yüksek lisans tezi), Fırat Üniversitesi, Elazığ.
- Escontrela, A. (2018). Convolutional neural networks from the ground up. Erişim tarihi: 23.08.2019. https://towardsdatascience.com/convolutional-neural-networks-from-the-ground-up-c67bb41454e1.
- Ghiassi, M., Olschimke, M., Moon, B., & Arnaudo, P. (2012). Automated text classification using a dynamic artificial neural network model. Expert Systems with Applications, 39(12), 10967-10976.
- Gülcü, A. & Kuş, Z. (2019). Konvolüsyonel sinir ağlarında hiper-parametre optimizasyonu yöntemlerinin incelenmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 7(2), 503-522.
- Han, J., Kamber, M., & Pei, J. (2011). Data mining concepts and techniques third edition. The Morgan Kaufmann Series in Data Management Systems, 83-124. Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
- Kaminski, J. (2014). Nowcasting the Bitcoin market with twitter signals. arXiv preprint arXiv:1406.7577.
- Kinderis, M., Bezbradica, M., & Crane, M. (2018). Bitcoin Currency Fluctuation. 3rd International Conference on Complexity, Future Information Systems and Risk, 31-41
- Kingma, D. P. & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
- Khan, R., Khan, H. U., Faisal, M. S., Iqbal, K., & Malik, M. S. I. (2016). An analysis of Twitter users of Pakistan. International Journal of Computer Science and Information Security, 14(8), 855-864.
- Kouloumpis, E., Wilson, T., & Moore, J. (2011). Twitter sentiment analysis: The good the bad and the omg!. Fifth International AAAI conference on weblogs and social media. 538-541
- Lam, S. L., & Lee, D. L. (1999). Feature reduction for neural network based text categorization. Proceedings of 6th International Conference on Advanced Systems for Advanced Applications, 195-202
- Lee, S. (2004). Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environmental Management, 34(2), 223-232.
- Maron, M. E. (1961). Automatic indexing: an experimental inquiry. Journal of the ACM (JACM), 8(3), 404-417.
- Matta, M., Lunesu, I., & Marchesi, M. (2015). Bitcoin Spread Prediction Using Social and Web Search Media. Workshop Deep Content Analytics Techniques for Personalized & Intelligent Services, 1-10.
- Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
- Özkan, Y. (2016). Veri Madenciliği Yöntemleri. İstanbul: Papatya Bilim.
- Öztemel, E. (2016). Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık.
- Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
- Shah, D., & Zhang, K. (2014). Bayesian regression and Bitcoin. 52nd annual Allerton conference on communication, control, and computing (Allerton). 409-414
- Shintate, T. & Pichl, L. (2019). Trend prediction classification for high frequency bitcoin time series with deep learning. Journal of Risk and Financial Management, 12(1), 1-15.
- Vapnik, V.N & Vapnik, V. (1998). Statistical learning theory. Wiley: New York
- Wiener, E., Pedersen, J. O., & Weigend, A. S. (1995). A neural network approach to topic spotting. Proceedings of SDAIR-95, 4th annual symposium on document analysis and information retrieval, 317-332.
Metin Sınıflandırmada Yapay Sinir Ağları ile Bitcoin Fiyatları ve Sosyal Medyadaki Beklentilerin Analizi
Abstract
Son
yıllarda, bloglar, tweet’ler, forumlar, e-postalar gibi Web 2.0 hizmetleri
iletişim kanalı olarak yaygın bir şekilde kullanılmaktadır. Ayrıca sosyal
medya; gerek bilgi paylaşımı gerekse istek, şikayet ve dilekler gibi görüşleri
belirtmenin en kolay ve en güncel yolu olarak düşünülmektedir. Sosyal medyanın,
birçok alana olduğu gibi Bitcoin fiyatlarına olan etkisi de son yıllarda
tartışılmaktadır.
Bitcoin yıllardır
üzerinde durulan ve popülerliği her geçen gün artan bir yatırım aracıdır.
Merkezi olmayan bir elektronik para birimi sistemi olan Bitcoin, çok sayıda
kullanıcının ilgisini çeken, finansal sistemlerdeki köklü bir değişikliği ifade
etmektedir. Bu çalışmada sosyal medyanın, özellikle Twitter kanalından elde
edilen tweet’ler bazında, Bitcoin fiyatı ile etkileşimi ortaya konulmuştur.
Bunun için 06.10.2018-19.05.2019 tarihleri arasında Twitter kullanıcıları
tarafından atılan toplam 2.819.784 tweet üzerinden makine öğrenmesi
yöntemlerinden sınıflandırma algoritmaları kullanılarak çeşitli analizler
gerçekleştirilmiştir. Bulgular değerlendirildiğinde metin sınıflandırmada %90
ile en yüksek doğruluk oranına sahip olan Yapay Sinir Ağları kullanılmıştır.
Ayrıca Bitcoin fiyatları ve sınıflandırılmış olumlu/olumsuz tweet oranları ile
ikili korelasyon yapılmıştır. Elde edilen 0,681 korelasyon katsayısı ile
pozitif yönde orta üstü kuvvetli ilişki tespit edilmiştir.
References
- Alghobiri, M. (2019). Using data mining algorithm for sentiment analysis of users’ opinions about Bitcoin cryptocurrency. Journal of Theoretical and Applied Information Technology, 97(8), 2195-2205.
- Basu, A., Walters, C., & Shepherd, M. (2003). Support vector machines for text categorization. Proceedings of the 36th Annual Hawaii International Conference on System Sciences, 4(4), 1-8.
- Blanzieri, E. & Bryl, A. (2008). A survey of learning-based techniques of email spam filtering. Artificial Intelligence Review, 29(1), 63-92.
- Ceyhan, K., Kurtulmaz, E., Sert, O. C., & Özyer, T. (2018). Bitcoin movement prediction with text mining. 26th Signal Processing and Communications Applications Conference, 1-4.
- Cheuque Cerda, G. & L Reutter, J. (2019). Bitcoin price prediction through opinion mining. In Companion Proceedings of The 2019 World Wide Web Conference, 755-762.
- Colianni, S., Rosales, S. & Signorotti, M. (2015). Algorithmic trading of cryptocurrency based on Twitter sentiment analysis. Erişim Tarihi: 25.07.2019 http://cs229.stanford.edu/proj2015/029_report.pdf, ss. 1-5.
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
- Coşkun, C., & Baykal, A. (2011). Veri madenciliğinde sınıflandırma algoritmalarının bir örnek üzerinde karşılaştırılması. Akademik Bilişim, 1-8.
- Deng, X., Li, Y., Weng, J., & Zhang, J. (2019). Feature selection for text classification: A review. Multimedia Tools and Applications, 78(3), 3797-3816.
- Erdal, H. (2015). Makine öğrenmesi yöntemlerinin inşaat sektörüne katkısı: basınç dayanımı tahminlemesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 12(1), 109-114.
- Dumais S, Platt J, Heckerman D, Sahami M (1998) Inductive learning algorithms and representations for text categorization. In: Proceedings of the seventh international conference on Information and knowledge management. ACM, 148–155.
- Eriş, M. (2006). Derin öğrenme yöntemleri kullanarak adli bilişim incelemelerinde delil çıkarımının gerçekleştirilmesi. (Basılmamış yüksek lisans tezi), Fırat Üniversitesi, Elazığ.
- Escontrela, A. (2018). Convolutional neural networks from the ground up. Erişim tarihi: 23.08.2019. https://towardsdatascience.com/convolutional-neural-networks-from-the-ground-up-c67bb41454e1.
- Ghiassi, M., Olschimke, M., Moon, B., & Arnaudo, P. (2012). Automated text classification using a dynamic artificial neural network model. Expert Systems with Applications, 39(12), 10967-10976.
- Gülcü, A. & Kuş, Z. (2019). Konvolüsyonel sinir ağlarında hiper-parametre optimizasyonu yöntemlerinin incelenmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 7(2), 503-522.
- Han, J., Kamber, M., & Pei, J. (2011). Data mining concepts and techniques third edition. The Morgan Kaufmann Series in Data Management Systems, 83-124. Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
- Kaminski, J. (2014). Nowcasting the Bitcoin market with twitter signals. arXiv preprint arXiv:1406.7577.
- Kinderis, M., Bezbradica, M., & Crane, M. (2018). Bitcoin Currency Fluctuation. 3rd International Conference on Complexity, Future Information Systems and Risk, 31-41
- Kingma, D. P. & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
- Khan, R., Khan, H. U., Faisal, M. S., Iqbal, K., & Malik, M. S. I. (2016). An analysis of Twitter users of Pakistan. International Journal of Computer Science and Information Security, 14(8), 855-864.
- Kouloumpis, E., Wilson, T., & Moore, J. (2011). Twitter sentiment analysis: The good the bad and the omg!. Fifth International AAAI conference on weblogs and social media. 538-541
- Lam, S. L., & Lee, D. L. (1999). Feature reduction for neural network based text categorization. Proceedings of 6th International Conference on Advanced Systems for Advanced Applications, 195-202
- Lee, S. (2004). Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environmental Management, 34(2), 223-232.
- Maron, M. E. (1961). Automatic indexing: an experimental inquiry. Journal of the ACM (JACM), 8(3), 404-417.
- Matta, M., Lunesu, I., & Marchesi, M. (2015). Bitcoin Spread Prediction Using Social and Web Search Media. Workshop Deep Content Analytics Techniques for Personalized & Intelligent Services, 1-10.
- Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
- Özkan, Y. (2016). Veri Madenciliği Yöntemleri. İstanbul: Papatya Bilim.
- Öztemel, E. (2016). Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık.
- Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
- Shah, D., & Zhang, K. (2014). Bayesian regression and Bitcoin. 52nd annual Allerton conference on communication, control, and computing (Allerton). 409-414
- Shintate, T. & Pichl, L. (2019). Trend prediction classification for high frequency bitcoin time series with deep learning. Journal of Risk and Financial Management, 12(1), 1-15.
- Vapnik, V.N & Vapnik, V. (1998). Statistical learning theory. Wiley: New York
- Wiener, E., Pedersen, J. O., & Weigend, A. S. (1995). A neural network approach to topic spotting. Proceedings of SDAIR-95, 4th annual symposium on document analysis and information retrieval, 317-332.
There are 33 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | March 13, 2020 |
| Acceptance Date | February 6, 2020 |
| Published in Issue | Year 2020 Volume: 4 Issue: 1 |
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