Emotion Detection for Cryptocurrency Tweets Using Machine Learning Algorithms
Abstract
Cryptocurrencies, functioning as digital currencies, undergo regular fluctuations in the present market, reflecting the emotional aspect of the cryptocurrency realm. It is a well-established fact that sentiment is linked to Bitcoin and Ethereum values, employing a Twitter-based strategy to predict changes. While prospective Bitcoin returns do not display a correlation with emotional variables, indicators of emotions tend to anticipate Bitcoin exchange volume and return volatility. Emotions wield an influence over a broad spectrum of financial investor returns, thereby, potentially affecting market dynamics by triggering significant price shifts. The research delves into gauging emotional factors extracted from 2,050,202 posts on Bitcointalk.org, investigating how these emotions impact Bitcoin's price fluctuations. We have used a unified dataset named 'data F' in which all categories of emotions are consolidated. Subsequently, data preprocessing steps are implemented to cleanse the dataset. Two feature engineering techniques, namely TF-IDF and BoW are employed. The research explores ten supervised machine learning (ML) models as classifiers, with four of these models (LR, Stochastic Gradient Descent, SVM and GB) yielding the highest accuracy at 0.93%.
References
D. Garcia, C. J. Tessone, P. Mavrodiev & N. Perony, "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy", Journal of the Royal Society Interface, vol. 11, 2014.
J. Abraham, D. Higdon, J. Nelson and J. Ibarra, "Cryptocurrency price prediction using tweet volumes and sentiment analysis", SMU Data Science Review, vol. 1, no.3, 2018.
D. Kahneman and A. Tversky, "Prospect theory: An analysis of decision under risk", Handbook of the fundamentals of financial decision making: Part I., World Scientific. pp. 99-127, 2013.
F.F. Bocca and L.H.A. Rodrigues, "The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modeling", Computers and electronics in agriculture, no. 128, pp. 67-76, 2016.
A. Jafar, M.A. Islam, and A. Khan, "The Effect of Social Networks and Google on Consumers’ Buying Behavior in Dhaka City, Bangladesh", Global Journal of Management and Business Research, no. 17, 2017.
M. Keijsers, C. Bartneck, and H.S. Kazmi, "Cloud-based sentiment analysis for interactive agents", in Proceedings of the 7th International Conference on Human-Agent Interaction, 2019.
T.H. Nguyen, K. Shirai, and J. Velcin, "Sentiment analysis on social media for stock movement prediction", Expert Systems with Applications, vol. 42, pp. 9603-9611, 2015.
H. Sul, A.R. Dennis, and L.I. Yuan. "Trading on twitter: The financial information content of emotion in social media", 47th Hawaii International Conference on System Sciences, IEEE, 2014.
A. Mittal and A. Goel, "Stock prediction using twitter sentiment analysis", Standford University, CS229, vol. 15, pp. 2352, 2012.
J. Bollen, H. Mao and X. Zeng, "Twitter mood predicts the stock market", Journal of computational science, vol. 2, no. 1, pp. 1-8, 2011.
S.B. Kotsiantis, I. Zaharakis, and P. Pintelas, "Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering", vol. 160, no. 1, pp. 3-24, 2007.
C. Lamon, E. Nielsen, and E. Redondo, "Cryptocurrency price prediction using news and social media sentiment", SMU Data Sci. Rev, vol. 1, no. 3 pp. 1-22, 2017.
Z.H. Munim, M.H. Shakil, and I. Alon, "Next-day bitcoin price forecast", Journal of Risk and Financial Management, vol. 12, pp. 103, 2019.
B. M. Lucey and M. Dowling, "The role of feelings in investor decision‐making", Journal of economic surveys, vol. 19, no. 2, pp.211-237, 2005.
M.M. Seif, E.M. Ramzy, and G. Abdel, "Stock market real time recommender model using apache spark framework", The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Springer, 2018.
N. Aslam, F. Rustam, E. Lee, P.B. Washington and I. Ashraf, "Sentiment analysis and emotion detection on cryptocurrency related tweets using ensemble LSTM-GRU model", IEEE Access, vol. 10, pp. 39313-39324, 2022.
J. Heaton, "An empirical analysis of feature engineering for predictive modeling", in SoutheastCon 2016, IEEE, 2016.
S. Robertson, "Understanding inverse document frequency: on theoretical arguments for IDF", Journal of documentation, vol. 60, no. 5, pp. 503-520, 2004.
F. Rustam, I. Ashraf, A. Mehmood, S. Ullah, and G.S. Choi, "Tweets classification on the base of sentiments for US airline companies", Entropy, vol. 21, no. 11, pp. 1078, 2019.
D. Shah, H. Isah, and F. Zulkernine, "Predicting the effects of news sentiments on the stock market", IEEE International Conference on Big Data (Big Data), IEEE, 2018.
B. Spilak, "Deep neural networks for cryptocurrencies price prediction", Humboldt-Universität zu Berlin, 2018.
E. Stenqvist and J. Lönnö, "Predicting Bitcoin price fluctuation with Twitter sentiment analysis", 2017.
P.C. Tetlock, "Giving content to investor sentiment: The role of media in the stock market", The Journal of Finance, vol. 62, no. 3, pp. 1139-1168, 2007.
