Twitter'da Kripto Para Duygu Analizi için Geleneksel ve Bağlamsal Temsillerin Performans Karşılaştırması

Abstract

Son yıllarda, özellikle Twitter gibi platformlarda kripto para birimleri hakkındaki tartışmalar giderek yaygınlaşmaktadır. Bu çalışma, doğal dil işleme (NLP) tabanlı makine öğrenimi (ML) ve derin öğrenme (DL) metodolojilerini uygulayarak kripto paralarla ilgili tweetlerin duygu analizini (SA) yapmaya odaklanmaktadır. Bu araştırmada 2020 ve 2021 yılları arasında açık kaynaklardan toplanan toplam 10.000 tweet kullanılmıştır. Analiz öncesinde veri kümesi, emojiler, bağlantılar ve HTML kodları gibi metin dışı öğelerin kaldırıldığı ayrıntılı bir ön işlemden geçirilmiştir. Metin temsilleri oluşturmak için başlangıçta TF-IDF kullanılmıştır. Naïve Bayes (NB), Karar Ağacı (DT), Destek Vektör Makinesi (SVM) dahil olmak üzere çeşitli geleneksel makine öğrenimi modelleri uygulanmıştır. Çift Yönlü Uzun Kısa Süreli Bellek (BiLSTM) ve Çift Yönlü Geçitli Tekrarlayan Birim (BiGRU) dahil olmak üzere gelişmiş DL modelleri de kullanılmıştır. Bağlamsal ilişkileri daha etkili bir şekilde yakalamak için, Transformatörlerden Çift Yönlü Kodlayıcı Temsilleri (BERT) modeli tarafından üretilen kelime gömmede kullanılmıştır. Performans değerlendirildiğinde, BERT tabanlı BiGRU modeli en yüksek doğruluğu (%93) ve en iyi F1 puanını elde etmiştir. Bu, derin bağlama dayalı kelime gömmeleri sıralı örüntülerden öğrenme yeteneğine sahip modellerle birleştirmenin etkinliğini göstermektedir. Genel olarak bulgular, DL yaklaşımlarının, özellikle de BERT gibi gelişmiş temsil yöntemlerini içerenlerin, duygu sınıflandırma görevlerinde geleneksel modellerden önemli ölçüde daha iyi performans gösterebileceğini göstermektedir.

Keywords

• Kripto para
• duygu analizi
• kelime temsili
• ML
• DL

Performance Comparison of Traditional and Contextual Representations for Cryptocurrency Sentiment Analysis on Twitter

Abstract

In recent years, discussions about cryptocurrencies, particularly on platforms such as Twitter, have become increasingly prevalent. This study focuses on conducting a sentiment analysis (SA) of tweets related to cryptocurrencies, applying machine learning (ML) and deep learning (DL) methodologies based on natural language processing (NLP). This research used a total of 10,000 tweets collected from open sources between 2020 and 2021. Prior to analysis, the dataset underwent detailed pre-processing, during which non-textual elements such as emojis, links, and HTML codes were removed. TF-IDF was initially employed to generate text representations. Various traditional ML models were applied, including Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM). Advanced DL models were also used, including Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). To capture contextual relationships more effectively, text embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT) model were also utilised. When performance was evaluated, the BERT-based BiGRU model achieved the highest Accuracy (Acc) of 93% and the best F1 score. This demonstrates the effectiveness of combining deep contextual embeddings with models capable of learning from sequential patterns. Overall, the findings suggest that DL approaches, particularly those that incorporate advanced representation methods such as BERT, can significantly outperform traditional models in sentiment classification tasks.

Keywords

• Cryptocurrency
• sentiment analysis
• text representation
• ML
• DL

References

1. [1] S. Nakamoto, "Bitcoin: A peer-to-peer electronic cash system," Bitcoin. Accessed: Apr. 9, 2025. [Online]. Available: https://bitcoin.org/bitcoin.pdf.
2. [2] J. Bollen, H. Mao and X. Zeng, “Twitter mood predicts the stock market,” Journal of Computational Science, vol. 2, no. 1, pp. 1–8, 2011.
3. [3] Y. Liu and A. Tsyvinski, “Risks and returns of cryptocurrency,” The Review of Financial Studies, vol. 34, no. 6, pp. 2689–2727, 2021.
4. [4] F. Mai, Z. Shan, Q. Bai, X. (Shane) Wang and R. H. L. Chiang, “How does social media impact bitcoin value? A test of the silent majority hypothesis,” Journal of Management Information Systems, vol. 35, no. 1, pp. 19–52, 2018.
5. [5] A.-U. Islam, "Crypto tweets," Accessed: Apr. 9, 2025. [Online]. Available: https://www.kaggle.com/datasets/leoth9/crypto-tweets
6. [6] G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Information Processing & Management, vol. 24, no. 5, pp. 513–523, 1988.
7. [7] J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, no. 1, pp. 81–106, 1986.
8. [8] J. Devlin, M. W. Chang, K. Lee and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,”in NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, vol. 1, Minneapolis, USA, 2019, pp. 4171–4186.

9. [9] K. I. Roumeliotis, N. D. Tselikas and D. K. Nasiopoulos, "LLMs and NLP models in cryptocurrency sentiment analysis: a comparative classification study," Big Data and Cognitive Compututing, vol. 8, no. 6, 2024, Art. no. 63.
10. [10] K. Byc, S. C. Ilinca and R. R. Mukkamala, "The relationship between social media sentiment and Bitcoin price volatility," M.S. thesis, Copenhagen Bus. Sch., Copenhagen, Denmark, 2021. [Online]. Available: https://research.cbs.dk/files/71300950/1303955_Master_Thesis_Sep_2021.pdf
11. [11] C. A. Ramaputra, M. H. Z. Al Faroby and B. R. Lidiawaty, "Sentiment analysis of user reviews on cryptocurrency application: Evaluating the impact of dataset split scenarios using multinomial naive Bayes," The Indonesian Journal of Computer Science, vol. 13, no. 4, 2024.
12. [12] C. Mai, M. R. A. Khan, A. Nicholson and S. A. R. Abu-Bakar, "Using Sentiment Analysis to Predict Bitcoin Price," in 2018 4th International Conference on Computer and Information Sciences (ICCOINS), Kuala Lumpur, Malaysia, 2018, pp. 1–6.
13. [13] F. Alam, S. K. Joty and M. Imran, "Deep learning for sentiment analysis of social media texts," Information Processing & Management, vol. 57, no. 1, pp. 102-106, 2020.
14. [14] S. McNally, J. Roche and S. Caton, "Predicting the Price of Bitcoin Using Machine Learning," in 2018 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), Cambridge, UK, 2018, pp. 339–343.
15. [15] A. Abraham, A. Zeng and J. Zhang, "Twitter and Reddit sentiment analysis for cryptocurrency price prediction," in 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 4560–4565.
16. [16] S. N. Başa and M. S. Basarslan, “Sentiment analysis using machine learning techniques on IMDB dataset,” in 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Türkiye, 2023, pp. 1–5.
17. [17] A. Triyono and A. Faqih, “Implementation of the Naive Bayes Method in sentiment analysis of Spotify application reviews,” Journal of Artificial Intelligence and Engineering Applications (JAIEA), vol. 4, no. 2, 1091-1097, 2025.
18. [18] N. Hussain, A. Qasim, G. Mehak, O. Kolesnikova, A. Gelbukh and G. Sidorov, "Hybrid machine learning and deep learning approaches for insult detection in Roman Urdu text," AI, vol. 6, no. 2, pp. 33, 2025.
19. [19] S. Gayathri, A. Chandar R.S., Rithicagash J. and Guna A. "Integrating fuzzy approach in text mining and summarization," in 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), Goathgaun, Nepal, 2025, pp. 98-102.
20. [20] S. Hochreiter and J. Schmidhuber, “Long Short-Term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
21. [21] M. B. Çakı and M. S. Başarslan, “Classification of fake news using machine learning and deep learning,” Journal of Artificial Intelligence and Data Science, vol. 4, no. 1, pp. 22–32, 2024.
22. [22] D. Chi, “Research on electricity consumption forecasting model based on wavelet transform and multi-layer LSTM model,” Energy Reports, vol. 8, pp. 220–228, 2022.
23. [23] Y. Xiong, N. Wei, K. Qiao, Z. Li and Z. Li, "Exploring consumption intent in live e-commerce barrage: a text feature-based approach using BERT-BiLSTM model," IEEE Access, vol. 12, pp. 69288-69298, 2024.
24. [24] M. S. Başarslan, “M-C&M-BL: a novel classification model for brain tumor classification: multi-CNN and multi-BiLSTM,” The Journal of Supercomputing, vol. 81, no. 3, 2025, Art. no. 502.
25. [25] Y. Çelik, “Bellek tabanlı LSTM ve GRU makine öğrenmesi algoritmaları kullanarak BIST100 endeks tahmini”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 36, no. 2, pp. 553–561, 2024.
26. [26] M. S. Islam and N. A. Ghani, “A novel BiGRUBiLSTM model for multilevel sentiment analysis using deep neural network with BiGRU-BiLSTM,” Lecture Notes in Electrical Engineering, vol. 730, pp. 403–414, 2022.
27. [27] J. Yan, J. Liu, Y. Yu and H. Xu, “Water quality prediction in the Luan river based on 1-DRCNN and BiGRU Hybrid Neural Network Model,” Water (Basel), vol. 13, no. 9, 2021, Art. no. 1273.
28. [28] Z. Turgut and G. Akgün, "Occupancy and occupant number detection for energy saving in smart buildings via machine learning techniques," International Journal of Exergy, vol. 44, no. 3, pp. 204–226, 2024.
29. [29] M. U. Etli et al., “Evaluating deep learning techniques for detecting aneurysmal subarachnoid hemorrhage: A comparative analysis of convolutional neural network and transfer learning models,” World Neurosurg, vol. 187, pp. e807–e813, 2024.
30. [30] F. Pedregosa et al., "Scikit-learn: machine learning in Python," The Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.