Deep Learning and Machine Learning Based Sentiment Analysis on BitCoin (BTC) Price Prediction

Year 2022, , 1 - 17, 27.12.2022

Ayşenur Sarıkaya , Serpil Aslan

https://doi.org/10.46572/naturengs.1182766

Abstract

Emotions form an essential and fundamental aspect of our lives. What we do and say reflects some of our feelings in some way, though not directly. We must examine these feelings using emotional data, also known as affect data, to comprehend a person's basic behavior. Text, voice, facial expressions, and other data types can be included. Since social networking websites have become so popular, many individuals have started reading the material on these numerous sites.Twitter is one of these social networking sites. People's feelings and thoughts about a subject reveal positive, negative, and neutral emotional values. Doing sentiment analysis on Twitter is a very important and challenging task. In this study, we aim to investigate the sentiments of Bitcoin and provide an overview of its effect on the value of Bitcoin by utilizing the power of deep learning architectures and machine learning methods. The study collected tweets in English shared on Twitter between December 12, 2021, and March 13, 2022. First, people's feelings about Bitcoin were assessed using TextBlob, a natural language processing (NLP) tool. Then, it was done using basic machine learning algorithms for sentiment classification and CNN, LSTM, and BiLSTM deep learning architectures that we modeled. However, deep learning models were tested separately with the TF-IDF and Glove word embedding approaches. Experimental results prove the success of deep learning architectures using the Glove word embedding approach.

Keywords

BitCoin, Sentiment Analysis, Machine Learning, Deep Learning, Glove, TF-IDF, CNN, LSTM, BiLSTM.

References

• [1] Sánchez-Rada, J. F., & Iglesias, C. A. (2019). Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison. Information Fusion, 52, 344-356.
• [2] McNally, S., Roche, J., & Caton, S. (2018, March). Predicting the price of bitcoin using machine learning. In 2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP) (pp. 339-343). IEEE.
• [3] Kiritchenko, S., Zhu, X., & Mohammad, S. M. (2014). Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, 50, 723-762.
• [4] Hassonah, M. A., Al-Sayyed, R., Rodan, A., Ala’M, A. Z., Aljarah, I., & Faris, H. (2020). An efficient hybrid filter and evolutionary wrapper approach for sentiment analysis of various topics on Twitter. Knowledge-Based Systems, 192, 105353.
• [5] Jin, C., Kong, X., Chang, J., Cheng, H., & Liu, X. (2020). Internal crack detection of castings: a study based on relief algorithm and Adaboost-SVM. The International Journal of Advanced Manufacturing Technology, 108(9), 3313-3322.
• [6] Al Amrani, Y., Lazaar, M., & El Kadiri, K. E. (2018). Random forest and support vector machine based hybrid approach to sentiment analysis. Procedia Computer Science, 127, 511-520.
• [7] Nakamoto, S. (2009). Bitcoin: A peer-to-peer electronic cash system Bitcoin: A Peer-to-Peer Electronic Cash System. Bitcoin. org. Disponible en https://bitcoin. org/en/bitcoin-paper.
• [8] Miraz, M. H., & Ali, M. (2018). Applications of blockchain technology beyond cryptocurrency. arXiv preprint arXiv:1801.03528.
• [9] Kilimci, Z. H. (2020). Sentiment analysis based direction prediction in bitcoin using deep learning algorithms and word embedding models. International Journal of Intelligent Systems and Applications in Engineering, 8(2), 60-65.
• [10] Pant, D. R., Neupane, P., Poudel, A., Pokhrel, A. K., & Lama, B. K. (2018, October). Recurrent neural network based bitcoin price prediction by twitter sentiment analysis. In 2018 IEEE 3rd International Conference on Computing, Communication and Security.
• [11] Spencer, J., & Uchyigit, G. (2012, September). Sentimentor: Sentiment analysis of twitter data. In SDAD@ ECML/PKDD (pp. 56-66).
• [12] Kamyab, M., Liu, G., & Adjeisah, M. (2021). Attention-Based CNN and Bi-LSTM Model Based on TF-IDF and GloVe Word Embedding for Sentiment Analysis. Applied Sciences, 11(23), 11255.
• [13] Whitelaw, C., Garg, N., & Argamon, S. (2005, October). Using appraisal groups for sentiment analysis. In Proceedings of the 14th ACM international conference on Information and knowledge management (pp. 625-631).
• [14] We used MAXQDA 2020 (VERBI Software, 2019) for data analysis.
• [15] Loria, S. (2018). textblob Documentation. Release 0.15, 2, 269.