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Bitcoin Duygu Analizinde Duygu Tanıma ve Sınıflandırma için Makine Öğrenmesi Yaklaşımı

Year 2024, Volume: 29 Issue: 3, 913 - 926, 31.12.2024
https://doi.org/10.53433/yyufbed.1532649

Abstract

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.

References

  • Akhtar, Md. S., Gupta, D., Ekbal, A., & Bhattacharyya, P. (2017). Feature selection and ensemble construction: A two-step method for aspect-based sentiment analysis. Knowledge-Based Systems, 125, 116–135. https://doi.org/10.1016/j.knosys.2017.03.020
  • Alasmari, S. F., & Dahab, M. (2017). Sentiment detection, recognition and aspect identification. International Journal of Computer Applications, 177(2), 31-38. https://doi.org/10.5120/ijca2017915675
  • Alnaied, A., Elbendak, M., & Bulbul, A. (2020). An intelligent use of stemmer and morphology analysis for Arabic information retrieval. Egyptian Informatics Journal, 21(4), 209–217. https://doi.org/10.1016/j.eij.2020.02.004
  • Andhale, S., Mane, P., Vaingankar, D. C., Karia, K., & Talele, K. (2021). Twitter sentiment analysis for COVID-19. In 2021 International Conference on Communication Information and Computing Technology (ICCICT) (pp. 1-12), Mumbai, India. https://doi.org/10.1109/iccict50803.2021.9509933
  • Anonymous. (2023a). Bitcoin sentiment analysis | Twitter data. Kaggle. Access date: July 23, 2023. https://www.kaggle.com/datasets/gautamchettiar/bitcoin-sentiment-analysis-twitter-data
  • Anonymous. (2023b). Scikit-learn/scikit-learn: Scikit-learn 0.22.1. Access date: July 23, 2023. https://doi.org/10.5281/zenodo.3596890
  • Avcı, İ., & Koca, M. (2023). Predicting DDoS attacks using machine learning algorithms in building management systems. Electronics, 12(19), 4142. doi: https://doi.org/10.3390/ELECTRONICS12194142
  • Barros, D. P., Moura, J., Freire, C. R., Taleb, A. C., De Medeiros Valentim, R. A., & De Morais, P. S. G. (2020). Machine learning applied to retinal image processing for glaucoma detection: Review and perspective. Biomedical Engineering Online, 19(1), 20. https://doi.org/10.1186/s12938-020-00767-2
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
  • Bulu, B., Yağar, F., Kopmaz, B., Şişman Kitapçı, N., Kitapçı, O., Aksu Kılıç, P., Köksal, L., & Mumcu, G. (2019). The content of Twitter messages of different health groups: The role of social media in health. International Journal of Health Management and Tourism, 4(3), 228–236. https://doi.org/10.31201/ijhmt.644197
  • Cambria, E., Olsher, D., & Rajagopal, D. (2014). SENTICNeT 3: A common and common-sense knowledge base for cognition-driven sentiment analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8928
  • Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785
  • Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1). https://doi.org/10.1186/s12864-019-6413-7
  • Dass, S., Kannan, V. K., & Shyamala, K. (2020). Sentiment severity on location-based social network (LBSN) data of natural disasters. International Journal of Recent Technology and Engineering, 8(5), 6–12. https://doi.org/10.35940/ijrte.e6631.018520
  • Domingos, P., & Pazzani, M. (2017). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103-130. https://rdcu.be/dgWb2
  • Dutta, A., Kumar, S., & Basu, M. (2020). A Gated Recurrent Unit approach to Bitcoin price prediction. Journal of Risk and Financial Management, 13(2), 23. https://doi.org/10.3390/jrfm13020023
  • Elbagir, S., & Yang, J. (2019). Twitter sentiment analysis using natural language toolkit and VADER sentiment. In Proceedings of the International Multiconference of Engineers and Computer Scientists (Vol. 122, p. 16)
  • Fakieh, B., Al-Ghamdi, A. S. A.-M., Saleem, F., & Ragab, M. (2023). Optimal machine learning driven sentiment analysis on COVID-19 Twitter data. Computers, Materials & Continua, 75(1), 81–97. https://doi.org/10.32604/cmc.2023.033406
  • Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
  • Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D. N., & Giaglis, G. M. (2015). Using time-series and sentiment analysis to detect the determinants of Bitcoin prices. Social Science Research Network. https://doi.org/10.2139/ssrn.2607167
  • Gozbasi, O. (2021, July 12). Is Bitcoin a safe haven? A study on the factors that affect Bitcoin prices. International Journal of Economics and Financial Issues, 11(4), 35-40. https://econjournals.com/index.php/ijefi/article/view/11602
  • Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., & Donaldson, L. (2013). Use of sentiment analysis for capturing patient experience from free-text comments posted online. Journal of Medical Internet Research, 15(11), e239. https://doi.org/10.2196/jmir.2721
  • Hâkim, A., Erwin, A., Eng, K., Galinium, M., & Muliady, W. (2014). Automated document classification for news article in Bahasa Indonesia based on term frequency inverse document frequency (TF-IDF) approach. In 6th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 1–4).
  • Hasan, K. M. A., Shovon, S. D., Joy, N. H., & Islam, S. (2021). Automatic labeling of Twitter data for developing COVID-19 sentiment dataset. In 2021 5th International Conference on Electrical Information and Communication Technology (EICT) (pp. 1-6). https://doi.org/10.1109/eict54103.2021.9733548
  • Ibrahim, A. (2021). Forecasting the early market movement in Bitcoin using Twitter’s sentiment analysis: An ensemble-based prediction model. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1-5). https://doi.org/10.1109/iemtronics52119.2021.9422647
  • Joachims, T. (1999). Svmlight: Support vector machine. SVM-Light Support Vector Machine, University of Dortmund, 19(4), 25. http://svmlight.joachims.org/
  • Kinderis, M., Bezbradica, M., & Crane, M. (2018). Bitcoin currency fluctuation. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 31-41). https://doi.org/10.5220/0006794000310041
  • Kleinbaum, D. G., & Klein, M. (2002). Analysis of matched data using logistic regression. In Logistic Regression (pp. 227–265). Springer eBooks. https://doi.org/10.1007/0-387-21647-2_8
  • Kranjc, J., Smailović, J., Podpečan, V., Grčar, M., Žnidaršič, M., & Lavrač, N. (2015). Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform. Information Processing and Management, 51(2), 187–203. https://doi.org/10.1016/j.ipm.2014.04.001
  • Larkey, L. S., Ballesteros, L., & Connell, M. E. (2007). Light stemming for Arabic information retrieval. In: Soudi, A., Bosch, A.v., Neumann, G. (eds) Arabic computational morphology. Text, speech and language technology, vol 38. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6046-5_12
  • LaValley, M. P. (2008). Logistic regression. Circulation, 117(18), 2395–2399. https://doi.org/10.1161/circulationaha.106.682658
  • Li, T., Chamrajnagar, A. S., Fong, X. R., Rizik, N. R., & Fu, F. (2019). Sentiment-based prediction of alternative cryptocurrency price fluctuations using gradient boosting tree model. Frontiers in Physics, 7. https://doi.org/10.3389/fphy.2019.00098
  • Loria, S. (2018). Textblob Documentation (Release 0.15, 2[8], 269)
  • McGrath, M. (2023). Python in easy steps.Access date: 23.07.2023. https://openlibrary.org/books/OL26976831M/Python_in_easy_steps
  • Mitchell, R., & Frank, E. (2017). Accelerating the XGBoost algorithm using GPU computing. PeerJ, 3, e127. https://doi.org/10.7717/peerj-cs.127
  • Murphy, K. P. (2006). Naïve Bayes classifiers. University of British Columbia, 18(60), 1–8
  • Narkhede, S. (2018). Understanding AUC-ROC curve. Towards Data Science, 26(1), 220–227.
  • Neethu, M. S., & Rajasree, R. (2013). Sentiment analysis in Twitter using machine learning techniques. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-5). https://doi.org/10.1109/icccnt.2013.6726818
  • Nowozin, S., Rother, C., Bagon, S., Sharp, T., Yao, B., & Kohli, P. (2011). Decision tree fields. In International Conference on Computer Vision (pp. 1668–1675). https://doi.org/10.1109/iccv.2011.6126429
  • Patel, N., Parekh, B., Thakkar, N., Gupta, R., Tanwar, S., Sharma, G., & Sharma, R. (2022). Fusion in cryptocurrency price prediction: A decade survey on recent advancements, architecture, and potential future directions. IEEE Access, 10, 34511–34538. https://doi.org/10.1109/access.2022.3163023
  • Pradana, A. T., & Hayaty, M. (2019). The effect of stemming and removal of stopwords on the accuracy of sentiment analysis on Indonesian-language texts. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 4(4), 375–380. https://doi.org/10.22219/kinetik.v4i4.912
  • Quinlan, J. R. (1992). C4.5: Programs for Machine Learning. https://cds.cern.ch/record/2031749
  • Raaijmakers, J. G., & Shiffrin, R. M. (1992). Models for recall and recognition. Annual Review of Psychology, 43(1), 205–234.
  • Rahman, S., Hemel, J. N., Anta, S. J. A., & Muhee, H. A. (2018). Sentiment analysis using R: An approach to correlate Bitcoin price fluctuations with change in user sentiments. BRAC University Institutional Repository. http://dspace.bracu.ac.bd/xmlui/handle/10361/10163
  • Rigatti, S. J. (2017). Random Forest. Journal of Insurance Medicine, 47(1), 31–39. https://doi.org/10.17849/insm-47-01-31-39.1
  • Sallis, J., Gripsrud, G., Olsson, U., & Silkoset, R. (2021). Research methods and data analysis for business decisions. Springer International Publishing. https://doi.org/10.1007/978-3-030-84421-9
  • Sami, O., Elsheikh, Y., & Almasalha, F. (2021). The role of data pre-processing techniques in improving machine learning accuracy for predicting coronary heart disease. International Journal of Advanced Computer Science and Applications, 12(6). https://doi.org/10.14569/ijacsa.2021.0120695
  • Sammut, C., & Webb, G. I. (2011). Encyclopedia of Machine Learning. Springer Science & Business Media.
  • Shah, D., & Zhang, K. (2014). Bayesian regression and Bitcoin. In 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 409-414). https://doi.org/10.1109/allerton.2014.7028484
  • Suthaharan, S. (2016). Machine learning models and algorithms for big data classification. Springer Nature. https://doi.org/10.1007/978-1-4899-7641-3
  • Tanwar, S., Patel, N. A., Patel, S., Patel, J., Sharma, G., & Davidson, I. E. (2021). Deep Learning-Based Cryptocurrency Price Prediction Scheme with Inter-Dependent relations. IEEE Access, 9, 138633–138646. https://doi.org/10.1109/access.2021.3117848
  • Vumazonke, N., & Parsons, S. (2023). An analysis of South Africa’s guidance on the income tax consequences of crypto assets. South African Journal of Economic and Management Sciences, 26(1). https://doi.org/10.4102/sajems.v26i1.4832
  • Wang, H., Yao, Y., & Salhi, S. (2020). Tension in big data using machine learning: Analysis and applications. Technological Forecasting and Social Change, 158, 120175. https://doi.org/10.1016/j.techfore.2020.120175
  • Yogish, D., Manjunath, T. N., & Hegadi, R. S. (2019). Review on Natural Language Processing Trends and Techniques using NLTK. In Communications in Computer and Information Science (pp. 589–606). https://doi.org/10.1007/978-981-13-9187-3_53
  • Zhang, Y., Jin, R., & Zhou, Z. (2010). Understanding bag-of-words model: A statistical framework. International Journal of Machine Learning and Cybernetics, 1(1–4), 43–52. https://doi.org/10.1007/s13042-010-0001-0

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

Year 2024, Volume: 29 Issue: 3, 913 - 926, 31.12.2024
https://doi.org/10.53433/yyufbed.1532649

Abstract

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.

References

  • Akhtar, Md. S., Gupta, D., Ekbal, A., & Bhattacharyya, P. (2017). Feature selection and ensemble construction: A two-step method for aspect-based sentiment analysis. Knowledge-Based Systems, 125, 116–135. https://doi.org/10.1016/j.knosys.2017.03.020
  • Alasmari, S. F., & Dahab, M. (2017). Sentiment detection, recognition and aspect identification. International Journal of Computer Applications, 177(2), 31-38. https://doi.org/10.5120/ijca2017915675
  • Alnaied, A., Elbendak, M., & Bulbul, A. (2020). An intelligent use of stemmer and morphology analysis for Arabic information retrieval. Egyptian Informatics Journal, 21(4), 209–217. https://doi.org/10.1016/j.eij.2020.02.004
  • Andhale, S., Mane, P., Vaingankar, D. C., Karia, K., & Talele, K. (2021). Twitter sentiment analysis for COVID-19. In 2021 International Conference on Communication Information and Computing Technology (ICCICT) (pp. 1-12), Mumbai, India. https://doi.org/10.1109/iccict50803.2021.9509933
  • Anonymous. (2023a). Bitcoin sentiment analysis | Twitter data. Kaggle. Access date: July 23, 2023. https://www.kaggle.com/datasets/gautamchettiar/bitcoin-sentiment-analysis-twitter-data
  • Anonymous. (2023b). Scikit-learn/scikit-learn: Scikit-learn 0.22.1. Access date: July 23, 2023. https://doi.org/10.5281/zenodo.3596890
  • Avcı, İ., & Koca, M. (2023). Predicting DDoS attacks using machine learning algorithms in building management systems. Electronics, 12(19), 4142. doi: https://doi.org/10.3390/ELECTRONICS12194142
  • Barros, D. P., Moura, J., Freire, C. R., Taleb, A. C., De Medeiros Valentim, R. A., & De Morais, P. S. G. (2020). Machine learning applied to retinal image processing for glaucoma detection: Review and perspective. Biomedical Engineering Online, 19(1), 20. https://doi.org/10.1186/s12938-020-00767-2
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
  • Bulu, B., Yağar, F., Kopmaz, B., Şişman Kitapçı, N., Kitapçı, O., Aksu Kılıç, P., Köksal, L., & Mumcu, G. (2019). The content of Twitter messages of different health groups: The role of social media in health. International Journal of Health Management and Tourism, 4(3), 228–236. https://doi.org/10.31201/ijhmt.644197
  • Cambria, E., Olsher, D., & Rajagopal, D. (2014). SENTICNeT 3: A common and common-sense knowledge base for cognition-driven sentiment analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8928
  • Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785
  • Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1). https://doi.org/10.1186/s12864-019-6413-7
  • Dass, S., Kannan, V. K., & Shyamala, K. (2020). Sentiment severity on location-based social network (LBSN) data of natural disasters. International Journal of Recent Technology and Engineering, 8(5), 6–12. https://doi.org/10.35940/ijrte.e6631.018520
  • Domingos, P., & Pazzani, M. (2017). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103-130. https://rdcu.be/dgWb2
  • Dutta, A., Kumar, S., & Basu, M. (2020). A Gated Recurrent Unit approach to Bitcoin price prediction. Journal of Risk and Financial Management, 13(2), 23. https://doi.org/10.3390/jrfm13020023
  • Elbagir, S., & Yang, J. (2019). Twitter sentiment analysis using natural language toolkit and VADER sentiment. In Proceedings of the International Multiconference of Engineers and Computer Scientists (Vol. 122, p. 16)
  • Fakieh, B., Al-Ghamdi, A. S. A.-M., Saleem, F., & Ragab, M. (2023). Optimal machine learning driven sentiment analysis on COVID-19 Twitter data. Computers, Materials & Continua, 75(1), 81–97. https://doi.org/10.32604/cmc.2023.033406
  • Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
  • Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D. N., & Giaglis, G. M. (2015). Using time-series and sentiment analysis to detect the determinants of Bitcoin prices. Social Science Research Network. https://doi.org/10.2139/ssrn.2607167
  • Gozbasi, O. (2021, July 12). Is Bitcoin a safe haven? A study on the factors that affect Bitcoin prices. International Journal of Economics and Financial Issues, 11(4), 35-40. https://econjournals.com/index.php/ijefi/article/view/11602
  • Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., & Donaldson, L. (2013). Use of sentiment analysis for capturing patient experience from free-text comments posted online. Journal of Medical Internet Research, 15(11), e239. https://doi.org/10.2196/jmir.2721
  • Hâkim, A., Erwin, A., Eng, K., Galinium, M., & Muliady, W. (2014). Automated document classification for news article in Bahasa Indonesia based on term frequency inverse document frequency (TF-IDF) approach. In 6th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 1–4).
  • Hasan, K. M. A., Shovon, S. D., Joy, N. H., & Islam, S. (2021). Automatic labeling of Twitter data for developing COVID-19 sentiment dataset. In 2021 5th International Conference on Electrical Information and Communication Technology (EICT) (pp. 1-6). https://doi.org/10.1109/eict54103.2021.9733548
  • Ibrahim, A. (2021). Forecasting the early market movement in Bitcoin using Twitter’s sentiment analysis: An ensemble-based prediction model. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1-5). https://doi.org/10.1109/iemtronics52119.2021.9422647
  • Joachims, T. (1999). Svmlight: Support vector machine. SVM-Light Support Vector Machine, University of Dortmund, 19(4), 25. http://svmlight.joachims.org/
  • Kinderis, M., Bezbradica, M., & Crane, M. (2018). Bitcoin currency fluctuation. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 31-41). https://doi.org/10.5220/0006794000310041
  • Kleinbaum, D. G., & Klein, M. (2002). Analysis of matched data using logistic regression. In Logistic Regression (pp. 227–265). Springer eBooks. https://doi.org/10.1007/0-387-21647-2_8
  • Kranjc, J., Smailović, J., Podpečan, V., Grčar, M., Žnidaršič, M., & Lavrač, N. (2015). Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform. Information Processing and Management, 51(2), 187–203. https://doi.org/10.1016/j.ipm.2014.04.001
  • Larkey, L. S., Ballesteros, L., & Connell, M. E. (2007). Light stemming for Arabic information retrieval. In: Soudi, A., Bosch, A.v., Neumann, G. (eds) Arabic computational morphology. Text, speech and language technology, vol 38. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6046-5_12
  • LaValley, M. P. (2008). Logistic regression. Circulation, 117(18), 2395–2399. https://doi.org/10.1161/circulationaha.106.682658
  • Li, T., Chamrajnagar, A. S., Fong, X. R., Rizik, N. R., & Fu, F. (2019). Sentiment-based prediction of alternative cryptocurrency price fluctuations using gradient boosting tree model. Frontiers in Physics, 7. https://doi.org/10.3389/fphy.2019.00098
  • Loria, S. (2018). Textblob Documentation (Release 0.15, 2[8], 269)
  • McGrath, M. (2023). Python in easy steps.Access date: 23.07.2023. https://openlibrary.org/books/OL26976831M/Python_in_easy_steps
  • Mitchell, R., & Frank, E. (2017). Accelerating the XGBoost algorithm using GPU computing. PeerJ, 3, e127. https://doi.org/10.7717/peerj-cs.127
  • Murphy, K. P. (2006). Naïve Bayes classifiers. University of British Columbia, 18(60), 1–8
  • Narkhede, S. (2018). Understanding AUC-ROC curve. Towards Data Science, 26(1), 220–227.
  • Neethu, M. S., & Rajasree, R. (2013). Sentiment analysis in Twitter using machine learning techniques. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-5). https://doi.org/10.1109/icccnt.2013.6726818
  • Nowozin, S., Rother, C., Bagon, S., Sharp, T., Yao, B., & Kohli, P. (2011). Decision tree fields. In International Conference on Computer Vision (pp. 1668–1675). https://doi.org/10.1109/iccv.2011.6126429
  • Patel, N., Parekh, B., Thakkar, N., Gupta, R., Tanwar, S., Sharma, G., & Sharma, R. (2022). Fusion in cryptocurrency price prediction: A decade survey on recent advancements, architecture, and potential future directions. IEEE Access, 10, 34511–34538. https://doi.org/10.1109/access.2022.3163023
  • Pradana, A. T., & Hayaty, M. (2019). The effect of stemming and removal of stopwords on the accuracy of sentiment analysis on Indonesian-language texts. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 4(4), 375–380. https://doi.org/10.22219/kinetik.v4i4.912
  • Quinlan, J. R. (1992). C4.5: Programs for Machine Learning. https://cds.cern.ch/record/2031749
  • Raaijmakers, J. G., & Shiffrin, R. M. (1992). Models for recall and recognition. Annual Review of Psychology, 43(1), 205–234.
  • Rahman, S., Hemel, J. N., Anta, S. J. A., & Muhee, H. A. (2018). Sentiment analysis using R: An approach to correlate Bitcoin price fluctuations with change in user sentiments. BRAC University Institutional Repository. http://dspace.bracu.ac.bd/xmlui/handle/10361/10163
  • Rigatti, S. J. (2017). Random Forest. Journal of Insurance Medicine, 47(1), 31–39. https://doi.org/10.17849/insm-47-01-31-39.1
  • Sallis, J., Gripsrud, G., Olsson, U., & Silkoset, R. (2021). Research methods and data analysis for business decisions. Springer International Publishing. https://doi.org/10.1007/978-3-030-84421-9
  • Sami, O., Elsheikh, Y., & Almasalha, F. (2021). The role of data pre-processing techniques in improving machine learning accuracy for predicting coronary heart disease. International Journal of Advanced Computer Science and Applications, 12(6). https://doi.org/10.14569/ijacsa.2021.0120695
  • Sammut, C., & Webb, G. I. (2011). Encyclopedia of Machine Learning. Springer Science & Business Media.
  • Shah, D., & Zhang, K. (2014). Bayesian regression and Bitcoin. In 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 409-414). https://doi.org/10.1109/allerton.2014.7028484
  • Suthaharan, S. (2016). Machine learning models and algorithms for big data classification. Springer Nature. https://doi.org/10.1007/978-1-4899-7641-3
  • Tanwar, S., Patel, N. A., Patel, S., Patel, J., Sharma, G., & Davidson, I. E. (2021). Deep Learning-Based Cryptocurrency Price Prediction Scheme with Inter-Dependent relations. IEEE Access, 9, 138633–138646. https://doi.org/10.1109/access.2021.3117848
  • Vumazonke, N., & Parsons, S. (2023). An analysis of South Africa’s guidance on the income tax consequences of crypto assets. South African Journal of Economic and Management Sciences, 26(1). https://doi.org/10.4102/sajems.v26i1.4832
  • Wang, H., Yao, Y., & Salhi, S. (2020). Tension in big data using machine learning: Analysis and applications. Technological Forecasting and Social Change, 158, 120175. https://doi.org/10.1016/j.techfore.2020.120175
  • Yogish, D., Manjunath, T. N., & Hegadi, R. S. (2019). Review on Natural Language Processing Trends and Techniques using NLTK. In Communications in Computer and Information Science (pp. 589–606). https://doi.org/10.1007/978-981-13-9187-3_53
  • Zhang, Y., Jin, R., & Zhou, Z. (2010). Understanding bag-of-words model: A statistical framework. International Journal of Machine Learning and Cybernetics, 1(1–4), 43–52. https://doi.org/10.1007/s13042-010-0001-0
There are 55 citations in total.

Details

Primary Language English
Subjects Algorithms and Calculation Theory, Data Structures and Algorithms
Journal Section Engineering and Architecture / Mühendislik ve Mimarlık
Authors

Erol Kına 0000-0002-7785-646X

Emre Biçek 0000-0001-6061-9372

Publication Date December 31, 2024
Submission Date August 13, 2024
Acceptance Date September 23, 2024
Published in Issue Year 2024 Volume: 29 Issue: 3

Cite

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