Retracted : Bitcoin Price Prediction and NFT Generator Based on Sentiment Analysis
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Abstract
Twitter sentiment has been found to be useful in predicting whether the price of Bitcoin will rise or fall will climb or decline. Modelling market activity and hence emotion in the Bitcoin ecosystem gives insight into Bitcoin price forecasts. We take into account not just the emotion retrieved not just from tweets, but also from the quantity of tweets. With the goal of optimising time window within which expressed emotion becomes a credible predictor of price change, we provide data from research that examined the link among both sentiment and future price at various temporal granularities. We demonstrate in this study that not only can price direction be anticipated, but also the magnitude of price movement with same accuracy, and this is the study's major scientific contribution. Non-Fungible Token (NFT) has gained international interest in recent years as a blockchain-based application. The most prevalent kind of NFT that can be stored on many blockchains is digital art. We did studies on CryptoPunks, the most popular collection on the NFT market, in examine and depict each and every major ethical challenges. We investigated ethical concerns from three perspectives: design, trade transactions, and relevant Twitter topics. Using Python libraries, a Twitter crawler, and sentiment analysis tools, we scraped data from Twitter and performed the analysis and prediction on bitcoin and NFTs.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
References
- Nakamoto, S. (2008). “Bitcoin: A Peer-to-peer Electronic Cash System”. Www.Bitcoin.Org. Retrieved from Bitcoin.Org.
- D. Shah, H. Isah, and F. Zulkernine, “Stock market analysis: a review and taxonomy of prediction techiques,” International Journal of Financial Studies, vol. 7, no. 26, pp. 1–22, 2019. DOI: https://doi.org/10.3390/ijfs7020026
- K. Fu and A. Rosenfeld, “Pattern Recognition and Image Processing,” IEEE Transactions on Computers, vol. 25, pp. 1336–1346, 1976. DOI: https://doi.org/10.1109/TC.1976.1674602
- B. Zhou and J. Hu, “A dynamic pattern recognition approach based on Neural Network for stock time-series,” presented at the World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009, pp. 1552– 1555. DOI: https://doi.org/10.1109/NABIC.2009.5393674
- J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” Journal of Computational Science, vol. 2, pp. 1–8, 2011. DOI: https://doi.org/10.1016/j.jocs.2010.12.007
- Balcilar, M., Bouri, E., Gupta, R., Roubaud, D. “Can volume predict Bitcoin returns and volatility? A quantiles-based approach”. Econ. Model. 64, 74–81 (2017). DOI: https://doi.org/10.1016/j.econmod.2017.03.019
- Kim, Y.B., Lee, J., Park, N., Choo, J., Kim, J.H., Kim, C.H. (2017). “When Bitcoin encounters information in an online forum: using text mining to analyse user opinions and predict value fluctuation”. DOI: https://doi.org/10.1371/journal.pone.0177630
- Jacques Vella Critien, Albert Gatt and Joshua Ellul. “Bitcoin price change and trend prediction through twitter sentiment and data volume”. Critien et al. Financial Innovation (2022). DOI: https://doi.org/10.1186/s40854-022-00352-7
- Li Y, Dai W (2020). “Bitcoin price forecasting method based on CNN-LSTM hybrid neural network model”. DOI: https://doi.org/10.1049/joe.2019.1203
- Pant D, Neupane P, Poudel A, Pokhrel A, Lama B (2018) “Recurrent neural network-based bitcoin price prediction by twitter sentiment analysis”. DOI: https://doi.org/10.1109/CCCS.2018.8586824
- Serafni G, Yi P, Zhang Q, Brambilla M, Wang J, Hu Y, Li B (2010) “Sentiment-driven price prediction of the bitcoin based on statistical and deep learning approaches”. In: 2020 International joint conference on neural networks, IJCNN 2020, Glasgow, United Kingdom, July 19–24, 2020, pp. 1–8. IEEE (2020). DOI: https://doi.org/10.1109/IJCNN48605.2020.9206704
- Valencia F, Gómez-Espinosa A, Valdes B (2019) “Price movement prediction of cryptocurrencies using sentiment analysis and machine learning”. Entropy 21:1–12 DOI: https://doi.org/10.3390/e21060589
- Otabek Sattarov,Heung Seok Jeon,Ryumduck Oh,Jun Dong Lee. “Forecasting Bitcoin Price Fluctuation by Twitter Sentiment Analysis”. 2020 International Conference on Information Science and Communications Technologies (ICISCT) | 978-1-7281-9969-6/20/$31.00 ©2020 IEEE | DOI: 10.1109/ICISCT50599.2020.935152 DOI: https://doi.org/10.1109/ICISCT50599.2020.9351527
- Sakib Shahriar, Kadhim Hayawi. “NFTGAN: Non-Fungible Token Art Generation Using Generative Adversarial Networks”.
- Yufan Zhang, Zichao Chen, Luyao Zhang, Xin Tong. “Visualizing Non-Fungible Token Ethics: A Case Study on CryptoPunks”.