Course Materials

Books

There is no single textbook. Here are a few reference books - my teaching material will be based on these. We will try to rely on publicly accessible resources for as much as possible.

• “Python for Everybody” by Charles Severance (For Python overview)
• NLTK book (For NLP with Python overview)
• “Practical Natural Language Processing” by Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta and Harshit Surana. The book is also available for free on O’Reilly’s online learning platform (if your university is subscribed).
• “Speech and Language Processing” by Jurafsky and Martin 2nd Edition. 3rd Edition

One book that may be useful is this 2022 book: “Text as Data: A New Framework for Machine Learning and the Social Sciences” by Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart, published by Princeton University Press. The Google Books Preview seems interesting.

This paper gives a good survey of how NLP methods are used in Economics and related research areas: Gentzkow, M., Kelly, B., & Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3), 535-74.

Related papers

The following is a list of papers at the intersection of NLP and Economics I compiled from some past readings and exploring google scholar’s list of 20 most cited Economics journals. The list is neither complete nor objective and I did not read everything. A lot of these are published in NLP venues. You are free to choose any other relevant paper for the group discussions. You can find links to these by searching for titles on Google Scholar.

(Organized chronologically, latest first)

1. Ellingsen, J., Larsen, V. H., & Thorsrud, L. A. (2022). News media versus FRED‐MD for macroeconomic forecasting. Journal of Applied Econometrics, 37(1), 63-81.

2. Zhai, S., & Zhang, Z. (2022). Read the News, Not the Books: Forecasting Firms’ Long-term Financial Performance via Deep Text Mining. ACM Transactions on Management Information Systems (TMIS).

3. Lutz, B., Pröllochs, N., & Neumann, D. (2022). Are longer reviews always more helpful? Disentangling the interplay between review length and line of argumentation. Journal of Business Research, 144, 888-901.

4. Mansouri, S., & Momtaz, P. P. (2022). Financing sustainable entrepreneurship: ESG measurement, valuation, and performance. Journal of Business Venturing, 37(6), 106258.

5. Ferrario, B., & Stantcheva, S. (2022, May). Eliciting People’s First-Order Concerns: Text Analysis of Open-Ended Survey Questions. In AEA Papers and Proceedings (Vol. 112, pp. 163-69).

6. Frankel, R., Jennings, J., & Lee, J. (2022). Disclosure sentiment: machine learning vs. dictionary methods. Management Science, 68(7), 5514-5532.

7. Caldara, Dario and Matteo Iacoviello (2022). Measuring Geopolitical Risk, International Finance Discussion Papers 1222r1. Washington: Board of Governors of the Federal Reserve System, https://doi.org/10.17016/IFDP.2022.1222r1

8. Nathan, M., & Rosso, A. (2022). Innovative events: product launches, innovation and firm performance. Research Policy, 51(1), 104373.

9. Kalamara, E., Turrell, A., Redl, C., Kapetanios, G., & Kapadia, S. (2022). Making text count: economic forecasting using newspaper text. Journal of Applied Econometrics, 37(5), 896-919.

10. Maximilian Ahrens and Michael McMahon. 2021. Extracting Economic Signals from Central Bank Speeches. In Proceedings of the Third Workshop on Economics and Natural Language Processing, pages 93–114, Punta Cana, Dominican Republic. Association for Computational Linguistics.

11. Davis, S. J., Hansen, S., & Seminario-Amez, C. (2020). Firm-level risk exposures and stock returns in the wake of COVID-19 (No. w27867). National Bureau of Economic Research.

12. Kong, N., Dulleck, U., Jaffe, A. B., Sun, S., & Vajjala, S. (2020). Linguistic metrics for patent disclosure: Evidence from university versus corporate patents (No. w27803). National Bureau of Economic Research.

13. Baylis, P. (2020). Temperature and temperament: Evidence from Twitter. Journal of Public Economics, 184, 104161.

14. Ros, R., van Erp, M., Rijpma, A., & Zijdeman, R. (2020). Mining Wages in Nineteenth-Century Job Advertisements. The Application of Language Resources and Language Technology to study Economic and Social Inequality. In Proceedings of the Workshop about Language Resources for the SSH Cloud (pp. 27-32).

15. Moreno-Ortiz, A., Fernandez-Cruz, J., & Hernández, C. P. C. (2020). Design and Evaluation of SentiEcon: a fine-grained Economic/Financial Sentiment Lexicon from a Corpus of Business News. In Proceedings of The 12th Language Resources and Evaluation Conference (pp. 5065-5072).

16. Masson, C., & Paroubek, P. (2020). Nlp analytics in finance with dore: A french 250m tokens corpus of corporate annual reports. In Proceedings of The 12th Language Resources and Evaluation Conference (pp. 2261-2267).

17. Qin, Y., & Yang, Y. (2019). What you say and how you say it matters: Predicting stock volatility using verbal and vocal cues. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 390-401).

18. Zamani, M., & Schwartz, H. A. (2017, April). Using twitter language to predict the real estate market. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers (pp. 28-33).

19. Lefever, E., & Hoste, V. (2016, May). A classification-based approach to economic event detection in dutch news text. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16) (pp. 330-335).

20. Jelveh, Z., Kogut, B., & Naidu, S. (2014, October). Detecting latent ideology in expert text: Evidence from academic papers in economics. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1804-1809).

21. Takala, P., Malo, P., Sinha, A., & Ahlgren, O. (2014, May). Gold-standard for Topic-specific Sentiment Analysis of Economic Texts. In LREC (Vol. 2014, pp. 2152-2157).

22. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of Finance, 66(1), 35-65.

23. Li, F. (2008). Annual report readability, current earnings, and earnings persistence. Journal of Accounting and economics, 45(2-3), 221-247.

24. Ghose, A., Ipeirotis, P., & Sundararajan, A. (2007, June). Opinion mining using econometrics: A case study on reputation systems. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (pp. 416-423).

25. Brekke, M., Innselset, K., Kristiansen, M., & Øvsthus, K. (2006, May). Automatic Term Extraction from Knowledge Bank of Economics. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06).