[1] Coindesk, “Bitcoin Price,” 2020. https://www.coindesk.com/price/bitcoin
[2] S. Corbet, A. Meegan, C. Larkin, B. Lucey, and L. Yarovaya, “Exploring the Dynamic Relationships between Cryptocurrencies and Other Financial Assets,” Economics Letters, vol. 165, no. 1, pp. 28–34, 2018.
[3] O. Kraaijeveld and J. De Smedt, “The predictive power of public Twitter sentiment for forecasting cryptocurrency prices,” Journal of International Financial Markets, Institutions and Money, vol. 65, p. 101188, 2020, doi: 10.1016/j.intfin.2020.101188.
[4] Z. Chen, C. Li, and W. Sun, “Bitcoin price prediction using machine learning: An approach to sample dimension engineering,” Journal of Computational and Applied Mathematics, vol. 365, p. 112395, 2020, doi: 10.1016/j.cam.2019.112395.
[5] 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, Jan. 2018, doi: 10.1080/07421222.2018.1440774.
[6] R. Grinberg, “Bitcoin: An Innovative Alternative Digital Currency (Preliminary Draft),” Hastings Science & Technology Law Journal, no. May, pp. 1–44, 2011.
[7] M. Kiran and M. Stannett, “Bitcoin risk analysis,” nemode.ac.uk, 2014.
[8] E. Erdin, M. Cebe, K. Akkaya, S. Solak, and E. Bulut, “A Bitcoin payment network with re duce d transaction fees and confirmation times,” Computer Networks, vol. 172, p. 107098, 2020, doi: 10.1016/j.comnet.2020.107098.
[9] A. Khadivar and F. Abbasi, “Method for Measuring Information Technology Program Risk by Fuzzy Approach,” BI Management Studies, vol. 1, no. 3, pp. 47–69, 2014.
[10] ISO, ISO 31000:2018. Risk management - Principles and guidelines. 2018.
[11] A. Vafadar Nikjoo, A. Shahabi, and S. M. A. Khatami Firouzabadi, “Determining most significant project risk’s categories with considering causal relations between them in the fuzzy environment,” Management Research in Iran, vol. 17, no. 3, pp. 49–69, 2021, [Online]. Available: https://mri.modares.ac.ir/article_158.html
[12] R. Ali, J. Barrdear, R. Clews, and J. Southgate, “The economies of digital currencies,” Bank of England Quarterly Bulletin, vol. 43, no. 3, pp. 276–286, 2014.
[13] C. Lustig and B. Nardi, “Algorithmic authority: The case of Bitcoin,” Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 2015-March, pp. 743–752, 2015, doi: 10.1109/HICSS.2015.95.
[14] C. Baek and M. Elbeck, “Bitcoins as an investment or speculative vehicle? A first look,” Applied Economics Letters, vol. 22, no. 1, pp. 30–34, 2015, doi: 10.1080/13504851.2014.916379.
[15] Turpin, “Bitcoin: The Economic Case for a Global, Virtual Currency Operating in an Unexplored Legal Framework,” Indiana Journal of Global Legal Studies, vol. 21, no. 1, p. 335, 2014, doi: 10.2979/indjglolegstu.21.1.335.
[16] C. Y. Wu and V. K. Pandey, “The value of Bitcoin in enhancing the efficiency of an investor’s portfolio,” Journal of Financial Planning, vol. 27, no. 9, pp. 44–52, 2014.
[17] mahin sabet sarvestani, abbas moghbel baarz, and A. Afsar, “Comparative analysis of the structural attributes of supply network firms in auto industry (social network analysis approach),” Modern Research in Decision Making, vol. 4, no. 4, pp. 59–80, 2019, [Online]. Available: http://journal.saim.ir/article_37598.html
[18] L. Kristoufek, “BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era,” Scientific Reports, vol. 3, pp. 1–7, 2013, doi: 10.1038/srep03415.
[19] C. Lamon, E. Nielsen, and E. Redondo, “Cryptocurrency Price Prediction Using News and Social Media Sentiment,” Pdfs.Semanticscholar.Org, 2016.
[20] L. Ege, “Predicting Bitcoin Price Fluctuations Based on News Headlines,” 2017.
[21] E. Stenqvist and J. Lönnö, “Predicting Bitcoin price fluctuation with Twitter sentiment analysis,” 2017.
[22] C. Eom, T. Kaizoji, S. Hoon, and L. Pichl, “Bitcoin and investor sentiment : Statistical characteristics and predictability,” Physica A, vol. 514, pp. 511–521, 2019, doi: 10.1016/j.physa.2018.09.063.
[23] D. Shen, A. Urquhart, and P. Wang, “Does twitter predict Bitcoin ?,” Economics Letters, vol. 174, pp. 118–122, 2019, doi: 10.1016/j.econlet.2018.11.007.
[24] O. Oueslati, E. Cambria, M. Ben HajHmida, and H. Ounelli, “A review of sentiment analysis research in Arabic language,” Future Generation Computer Systems, vol. 112, pp. 408–430, 2020, doi: 10.1016/j.future.2020.05.034.
[25] F. Abbasi, A. Khadivar, and M. Yazdinejad, “Sentiment Analysis of Users for Buying on Cell Phone in Digikala,” BI Management Studies, vol. 8, no. 32, pp. 181–210, 2020, doi: 10.22054/IMS.2020.46286.1585.
[26] S. Symeonidis, D. Effrosynidis, and A. Arampatzis, “A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis,” Expert Systems with Applications, vol. 110, 2018, doi: 10.1016/j.eswa.2018.06.022.
[27] T. Norsten, “Exploring the Potential of Twitter Data and Natural Language Processing Techniques to Understand the Usage of Parks in Stockholm,” 2020.
[28] S. Jung and W. C. Yoon, “An alternative topic model based on Common Interest Authors for topic evolution analysis,” Journal of Informetrics, vol. 14, no. 3, p. 101040, 2020, doi: 10.1016/j.joi.2020.101040.
[29] I. Vayansky and S. A. P. Kumar, “A review of topic modeling methods,” Information Systems, vol. 94, p. 101582, 2020, doi: 10.1016/j.is.2020.101582.
[30] M. Lamba, M. Madhusudhan, and I. Science, “Application of Topic Mining and Prediction Modeling Tools for Library and Information Science Journals,” pp. 395–401, 2018.
[31] W. Zhao et al., “A heuristic approach to determine an appropriate number of topics in topic modeling,” BMC Bioinformatics, vol. 16, no. 13, p. S8, 2015, doi: 10.1186/1471-2105-16-S13-S8.
[32] L. Hagen, “Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models?,” Information Processing and Management, vol. 54, no. 6, pp. 1292–1307, 2018, doi: 10.1016/j.ipm.2018.05.006.
[33] B. Sohrabi, I. Raeesi Vanani, and F. Zareh Mirkabad, “Designing a Recommender System for Optimizing and Managing Bank Facilities through the Utilization of Clustering and Classification Algorithms,” Modern Research in Decision Making, vol. 1, no. 2, pp. 53–76, 2016, [Online]. Available: http://journal.saim.ir/article_21125.html
[34] C. Yang, H. Zhang, B. Jiang, and K. Li, “Aspect-based sentiment analysis with alternating coattention networks,” Information Processing and Management, vol. 56, no. 3, pp. 463–478, 2019, doi: 10.1016/j.ipm.2018.12.004.
[35] A. zarei, davood feiz, and ghazale Taheri, “Providing Social Market Intelligence Framework based on web 2.0 Using Text-Mining Technique on Social Media Websites (Case Study: Competitive Analysis between Samsung and Emersun Brands),” Management Research in Iran, vol. 24, no. 4, pp. 98–125, 2021, [Online]. Available: https://mri.modares.ac.ir/article_540.html
[36] J. B. Han, S. H. Kim, M. H. Jang, and K. S. Ri, “Using Genetic Algorithm and NARX Neural Network to Forecast Daily Bitcoin Price,” Computational Economics, vol. 56, no. 2, pp. 337–353, 202