CRYPTO NARRATIVES SENTIMENT ANALYSIS ON BITCOIN PRICE PREDICTION USING THE NAIVE BAYES METHOD

Didik Nuryadi
Daniel H.F. Manongga
Irwan Sembiring


DOI: https://doi.org/10.29100/jipi.v10i2.6116

Abstract


Globalization affects many aspects of human life with consequences that may be positive or negative. Advances in information technology, which significantly assist many human activities, are one of the ele-ments affected. As a new product of financial technology, cryptocur-rency has revolutionized the global payment system. Bitcoin has expe-rienced significant price increases in recent years, often caused by eco-nomic and psychological market factors. Sentiment analysis of the bitcoin crypto narrative is essential for understanding market behavior and predicting price trends because market sentiment has been proven to influence bitcoin price movements. Therefore, this research aims to investigate the crypto sentiment narrative regarding Bitcoin price movements using a sentiment analysis approach with the Naïve Bayes classification method. The dataset used in this research comes from crypto narratives that are considered to influence bitcoin price move-ments, which were collected from October 2022 to April 2024. This re-search succeeded in classifying the data tested using 10-fold cross-validation testing, with an average of 76.13%. The precision score for the positive opinion class was 63.92%, and the precision score for the negative opinion class reached 81.77%. The average recall value for the positive class was 61.69%, and for the negative class, it reached 83.12%. This data shows that Naïve Bayes is quite good at analyzing crypto sentiment narratives regarding bitcoin price movements.

Keywords


Crypto Narrative; Bitcoin; Naive Bayes Clas-sifier; Data Mining; Sentiment

Full Text:

PDF

Article Metrics :

References


A. Wijayanto, I. Riadi, Y. Prayudi, And T. Sudinugraha, “Network Forensics Against Address Resolution Protocol Spoofing Attacks Using Trigger, Acquire, Analysis, Report, Action Method,” Regist. J. Ilm. Teknol. Sist. Inf., Vol. 8, No. 2, Pp. 156–169, 2022, Doi: 10.26594/Register.V8i2.2953.

S. Alam, M. Jamil, And A. Syamsir, “Digital Currency In Indonesia (Prospects And Challenges In Inclusive Financial Reviews),” J. Ad’ministrare, Vol. 9, No. 2, P. 515, 2022, Doi: 10.26858/Ja.V9i2.39498.

R. Iyer, “New Evidence On Spillovers Between Crypto Assets And Financial Markets,” Imf Work. Pap., Vol. 2023, No. 213, P. 1, 2023, Doi: 10.5089/9798400256622.001.

R. Shewale, “Blockchain Statistics,” Demandsage, 2024. Https://Www.Demandsage.Com/Blockchain-Statistics/

M. Campbell-Verduyn, Bitcoin And Beyond: Cryptocurrencies, Blockchains, And Global Governance. 2018. Doi: 10.4324/9781315211909.

O. Sattarov, H. S. Jeon, R. Oh, And J. D. Lee, “Forecasting Bitcoin Price Fluctuation By Twitter Sentiment Analysis,” 2020 Int. Conf. Inf. Sci. Commun. Technol. Icisct 2020, Vol. 02, No. 1, Pp. 1–12, 2020, Doi: 10.1109/Icisct50599.2020.9351527.

H. Jun Zhang, Y. Hui Chen, And H. Zhuo, “Unit Middleware For Implementation Of Human–Machine Interconnection Intelligent Ecology Construction,” J. Big Data, Vol. 10, No. 1, 2023, Doi: 10.1186/S40537-023-00787-4.

D. Horváth, “Money In The Digital Age: Exploring The Potential Of Central Bank Digital Currency With A Focus On Social Adaptation And Education,” Sustain. Futur., Vol. 6, No. June, 2023, Doi: 10.1016/J.Sftr.2023.100136.

L. Liestyowati, E. Sudarmanto, H. Ramadhani, S. Rijal, And T. W. Nurdiani, “Tren Investasi Aset Digital: Studi Tentang Perilaku Investor Muda Terhadap Cryptocurrency Di Tengah Perubahan Pasar Keuangan Di Kota Bandung,” J. Akunt. Dan Keuang. West Sci., Vol. 2, No. 03, Pp. 142–149, 2023, Doi: 10.58812/Jakws.V2i03.639.

F. Kjaerland, M. Meland, A. Oust, And V. Øyen, “How Can Bitcoin Price Fluctuations Be Explained?,” Int. J. Econ. Financ. Issues, Vol. 8, No. 3, Pp. 323–332, 2018, [Online]. Available: Http:Www.Econjournals.Com

E. T. Yolanda, L. Junaedi, And A. Bimo Gumelar, “Analisis Sentimen Pergerakan Harga Mata Uang Kripto (Cryptocurrency) Menggunakan Textblob-Nltk (Natural Language Toolkit),” Jlk, Vol. 5, No. 2, Pp. 44–50, 2022, [Online]. Available: Https://T.Co/Kbvjupjoyu

W. Purbaratri, H. D. Purnomo, D. Manongga, And I. Setyawan, “Sentiment Analysis Of Electronic Government Services Utilizing The Naive Bayes Algorithm,” Matrik J. Manajemen, Tek. Inform. Dan Rekayasa Komput., Vol. 23, No. 2, Pp. 441–452, 2024, Doi: 10.30812/Matrik.V23i2.3272.

K. A. Baihaqi Et Al., “A Comparison Support Vector Machine, Logistic Regression And Naïve Bayes For Classification Sentimen Analisys User Mobile App,” Int. J. Artif. Intell. Res., Vol. 7, No. 1, P. 64, 2023, Doi: 10.29099/Ijair.V7i1.962.

M. F. P. Alam, D. Manongga, I. Sembiring, And W. Sulistyo, “Sentiment Analysis For User Review Polri Hospital Registration App,” Proceeding Perbanas Int. Semin. Econ. Business, Manag. Account. It, Pp. 143–152, 2023.

Rsa, “2016: Current State Of Cybercrime,” P. 7, 2016, [Online]. Available: Https://Www.Rsa.Com/Content/Dam/Rsa/Pdf/2016/05/2016-Current-State-Of-Cybercrime.Pdf

A. Muhariya, A. Riadi, And I. Prayudi, “Cyberbullying Analysis On Instagram Using K-Means Clustering,” Juita J. Inform., Vol. 10, No. 2, Pp. 261–271, 2022, Doi: 10.30595/Juita.V10i2.14490.

I. Ruthven And M. Lalmas, “A Survey On The Use Of Relevance Feedback For Information Access Systems,” Knowl. Eng. Rev., Vol. 18, No. 2, Pp. 95–145, 2003, Doi: 10.1017/S0269888903000638.

S. K. Sahu, S. Sarangi, And S. K. Jena, “A Detail Analysis On Intrusion Detection Datasets,” Souvenir 2014 Ieee Int. Adv. Comput. Conf. Iacc 2014, Pp. 1348–1353, 2014, Doi: 10.1109/Iadcc.2014.6779523.

A. Muhariya, I. Riadi, Y. Prayudi, And I. A. Saputro, “Utilizing K-Means Clustering For The Detection Of Cyberbullying Within Instagram Comments,” Ing. Des Syst. D’information, Vol. 28, No. 4, Pp. 939–949, Aug. 2023, Doi: 10.18280/Isi.280414.

P. Yugianus, H. S. Dachlan, And R. N. Hasanah, “Pengembangan Sistem Penelusuran Katalog Perpustakaan Dengan Metode Rocchio Relevance Feedback,” J. Eeccis, Vol. 7, No. 1, Pp. 47–52, 2013.

H. M. A. Ishara Amali And S. Jayalal, “Classification Of Cyberbullying Sinhala Language Comments On Social Media,” Mercon 2020 - 6th Int. Multidiscip. Moratuwa Eng. Res. Conf. Proc., Pp. 266–271, 2020, Doi: 10.1109/Mercon50084.2020.9185209.