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Department of Informatics Blockchain and Distributed Ledger Technologies

Level: MA
Contact Person: Mostafa Chegeni
Keywords: Sentiment Analysis, Natural Language Processing, Time-Series Analysis, Blockchain Market Analysis

Cryptocurrencies have gained significant attention as alternative digital assets, leading to an evolving landscape of investors and traders. Social media platforms have become a crucial source of information and influence in this domain, with users expressing their opinions, sentiments, and preferences about various cryptocurrencies. This proposal aims to explore the relationship between social media sentiment and cryptocurrency market performance by employing sophisticated methodologies such as natural language processing (NLP) algorithms and time-series analysis methods.
The anticipated outcomes of this research include a deeper understanding of how social media sentiment influences cryptocurrency market performance. This research will contribute to the development of robust models that integrate NLP algorithms [1,2,3] and time-series analysis methods [4,5,6] for accurate predictions in the cryptocurrency market. Initially, we will review and compare existing social media datasets -- such as Kaggle [7], Stanford Large Network Dataset Collection (SNAP) [8], Twitter API [9], Reddit API [10], and Facebook Graph API [11] -- to choose the most suitable for our research objectives. Afterwards, by leveraging NLP algorithms, we will extract and analyze sentiment from social media posts, considering factors such as positive/negative sentiment, volume of mentions, and influential accounts. Finally, we will utilize time-series analysis methods to model the relationship between social media sentiment and cryptocurrency trade movements, enabling us to develop predictive models for both short- and long-term market performance.

[1] Aslam, N., Rustam, F., Lee, E., Washington, P.B. and Ashraf, I., 2022. Sentiment analysis and emotion detection on cryptocurrency related Tweets using ensemble LSTM-GRU Model. IEEE Access, 10, pp.39313-39324.
[2] Huang, X., Zhang, W., Tang, X., Zhang, M., Surbiryala, J., Iosifidis, V., Liu, Z. and Zhang, J., 2021. Lstm based sentiment analysis for cryptocurrency prediction. In Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11–14, 2021, Proceedings, Part III 26 (pp. 617- 621). Springer International Publishing.
[3] Ortu, M., Vacca, S., Destefanis, G. and Conversano, C., 2022. Cryptocurrency ecosystems and social media environments: An empirical analysis through Hawkes’ models and natural language processing. Machine Learning with Applications, 7, p.100229.
[4] Roy, S., Nanjiba, S. and Chakrabarty, A., 2018, December. Bitcoin price forecasting using time series analysis. In 2018 21st International Conference of Computer and Information Technology (ICCIT) (pp. 1-5). IEEE.
[5] Malladi, R.K. and Dheeriya, P.L., 2021. Time series analysis of cryptocurrency returns and volatilities. Journal of Economics and Finance, 45(1), pp.75-94.
[6] Fleischer, J.P., von Laszewski, G., Theran, C. and Parra Bautista, Y.J., 2022. Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory. Algorithms, 15(7), p.230.