PEBSA: Predicting Economic Behavior via Sentiment Analysis
PEBSA is a machine learning (ML) model that aims to analyze public sentiment towards America's economic performance and predict its future behavior. By collecting public comments and posts from several social media platforms, sentiment analysis techniques will be applied to classify comments based on positive or negative outlooks. The ML model will then incorporate macroeconomic reasoning to forecast future economic behavior. The project will focus on fair and unbiased testing by employing scientific sampling techniques during data collection processes. The outcomes of this research will provide valuable insights for policymakers, businesses, and investors, facilitating informed decision-making in the realm of economic performance analysis.
Abbasi, H. (2021, May 15). A beginner’s guide to machine learning using ML.NET. Geek Culture. Retrieved from Medium
Agrawal, A., Gans, J. S., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
Athey, S. (2018). The impact of machine learning on economics. Stanford University Graduate School of Business, National Bureau of Economic Research. Retrieved from NBER
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. https://doi.org/10.1016/j.jocs.2010.12.007
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
Bureau of Economic Analysis. (2024, May 30). U.S. economy at a glance. Retrieved from BEA.gov
Campbell, J. Y., & Moore, A. S. (2024). Overprecision in the survey of professional forecasters. University of California Press – Collabra: Psychology, 10(1), 5-23.
Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88(s1), 2-9.
Costola, M., et al. (2023). Machine learning sentiment analysis, COVID-19 news, and stock market reactions. Research in International Business and Finance, 64, 101881. https://doi.org/10.1016/j.ribaf.2023.101881
Coulombe, P. G., Leroux, M., Stevanovic, D., & Surprenant, S. (2019). How is machine learning useful for macroeconomic forecasting? Review of Economic Dynamics, 31, 156-168. https://doi.org/10.1016/j.red.2018.09.009
Desai, A. (2023, October 27). Machine learning for economics research: When, what and how. Bank of Canada. Retrieved from Bank of Canada
Foy et al. (2023). New tools are available to help reduce the energy that AI models deliver. MIT News.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Gottfried, J., Barthel, M., & Mitchell, A. (2024). Americans’ social media use and its impact on economic trends. Pew Research Center. Retrieved from Pew Research Center
Han, S., Pool, J., Tran, J., & Dally, W. (2015). Learning both weights and connections for efficient neural network. Advances in Neural Information Processing Systems, 28, 1135-1143.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
Kapoor, S. (2019, August 12). Artificial intelligence: Problems and limitations. IT Exchange. Retrieved from IT Exchange Web
Kumar, A. (2024, July 26). How do geographical limitations impact methodology? SciSpace. Retrieved from SciSpace
Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers.
Malladi, R., Nanduri, V., & Agrawal, A. (2022). Application of supervised machine learning techniques to forecast the COVID-19 U.S. recession and stock market crash. National Library of Medicine. Retrieved from PubMed
Microsoft. (2020). ML.NET Documentation. Retrieved from Microsoft ML.NET
Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106. https://doi.org/10.1257/jep.31.2.87
Northumbria University. (2024, February 2). Social media research threatened by new data limitations. Retrieved from Northumbria University
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019). Social data: Biases, methodological pitfalls, and ethical boundaries. Frontiers in Big Data, 2, 13. https://doi.org/10.3389/fdata.2019.00013
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. https://doi.org/10.1561/1500000011
Sasaki, Y. (2007). The truth of the F-measure. Technical report, School of Computer Science, University of Manchester. Retrieved from University of Manchester
Scikit-learn. (2019). Scikit-learn: Machine learning in Python. Retrieved from Scikit-learn.org
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., & Riedmiller, M. (2016). Deterministic policy gradient algorithms. Proceedings of the 31st International Conference on Machine Learning, 32, 387-395.
Smolic, H. (2022, December 15). How much data is needed for machine learning? Graphite Note. Retrieved from Graphite Note
Shtar, G., & Margel, S. (2017, July 31). Clustering and dimensionality reduction: Understanding the "magic" behind machine learning. Imperva Blog. Retrieved from Imperva
Stock, J. H., & Watson, M. W. (2001). Vector autoregressions. Journal of Economic Perspectives, 15(4), 101-115.
Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645-3650. https://doi.org/10.18653/v1/P19-1355
Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28. https://doi.org/10.1257/jep.28.2.
Copyright (c) 2024 International Journal of Engineering and Computer Science

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.