PEBSA: Predicting Economic Behavior via Sentiment Analysis

Vignesh Nagarajan; Aarav Mittal

doi:10.18535/ijecs/v13i12.4950

Articles

Total : PDF: 60 | Total views: 60

Vignesh Nagarajan, Aarav Mittal,

Article Date Published : 10 December 2024 | Page No.: 26656-26676 |

DOI https://doi.org/10.18535/ijecs/v13i12.4950

Online Metrics

Abstract

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.

Keywords: Machine Learning, Macroeconomic Forecasting, Sentiment Analysis, Big Data

References

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.

Downloads

Download data is not yet available.

Comments & Peer Review

Author's Affiliation

Vignesh Nagarajan

BASIS Phoenix
Google Scholar

Aarav Mittal

University of Illinois Urbana-Champaign
Google Scholar

Copyrights & License

International Journal of Engineering and Computer Science, 2024.

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

Article Details

Issue: Vol. 13 No. 12 (2024)

Page No.: 26656-26676

Section: Articles

DOI: https://doi.org/10.18535/ijecs/v13i12.4950

How to Cite

Nagarajan, V., & Mittal, A. (2024). PEBSA: Predicting Economic Behavior via Sentiment Analysis. International Journal of Engineering and Computer Science, 13(12), 26656–26676. https://doi.org/10.18535/ijecs/v13i12.4950

Download Citation

HTML Viewed - 172 Times
PDF Downloaded - 60 Times

PDF

Downloads

PDF

PEBSA: Predicting Economic Behavior via Sentiment Analysis

Abstract

References

Downloads

Author's Affiliation

Vignesh Nagarajan

Aarav Mittal

Copyrights & License

Article Details

How to Cite

Download Citation

Downloads

Sections

We Recommend