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Text as Data

Author

Listed:
  • Matthew Gentzkow
  • Bryan T. Kelly
  • Matt Taddy

Abstract

An ever increasing share of human interaction, communication, and culture is recorded as digital text. We provide an introduction to the use of text as an input to economic research. We discuss the features that make text different from other forms of data, offer a practical overview of relevant statistical methods, and survey a variety of applications.

Suggested Citation

  • Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:23276
    Note: AP CF IO POL
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    References listed on IDEAS

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    1. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    2. Grimmer, Justin, 2010. "A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases," Political Analysis, Cambridge University Press, vol. 18(1), pages 1-35, January.
    3. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    4. Stephens-Davidowitz, Seth, 2014. "The cost of racial animus on a black candidate: Evidence using Google search data," Journal of Public Economics, Elsevier, vol. 118(C), pages 26-40.
    5. Carlos M. Carvalho & Nicholas G. Polson & James G. Scott, 2010. "The horseshoe estimator for sparse signals," Biometrika, Biometrika Trust, vol. 97(2), pages 465-480.
    6. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
    7. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    8. James H. Stock & Francesco Trebbi, 2003. "Retrospectives: Who Invented Instrumental Variable Regression?," Journal of Economic Perspectives, American Economic Association, vol. 17(3), pages 177-194, Summer.
    9. Chris Hans, 2009. "Bayesian lasso regression," Biometrika, Biometrika Trust, vol. 96(4), pages 835-845.
    10. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    11. Jegadeesh, Narasimhan & Wu, Di, 2013. "Word power: A new approach for content analysis," Journal of Financial Economics, Elsevier, vol. 110(3), pages 712-729.
    12. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    13. Bradley Efron, 2004. "The Estimation of Prediction Error: Covariance Penalties and Cross-Validation," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 619-632, January.
    14. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    15. Tim Groseclose & Jeffrey Milyo, 2005. "A Measure of Media Bias," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(4), pages 1191-1237.
    16. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 119-135, National Bureau of Economic Research, Inc.
    17. Matt Taddy, 2013. "Rejoinder: Efficiency and Structure in MNIR," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 772-774, September.
    18. Matthias M M Buehlmaier & Toni M Whited, 2018. "Are Financial Constraints Priced? Evidence from Textual Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2693-2728.
    19. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference for High-Dimensional Sparse Econometric Models," Papers 1201.0220, arXiv.org.
    20. Matthew Gentzkow & Jesse M. Shapiro, 2010. "What Drives Media Slant? Evidence From U.S. Daily Newspapers," Econometrica, Econometric Society, vol. 78(1), pages 35-71, January.
    21. Matthew Gentzkow & Jesse M. Shapiro & Matt Taddy, 2019. "Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech," Econometrica, Econometric Society, vol. 87(4), pages 1307-1340, July.
    22. Matt Taddy, 2013. "Multinomial Inverse Regression for Text Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 755-770, September.
    23. Feng Li, 2010. "The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 48(5), pages 1049-1102, December.
    24. Cheryl J. Flynn & Clifford M. Hurvich & Jeffrey S. Simonoff, 2013. "Efficiency for Regularization Parameter Selection in Penalized Likelihood Estimation of Misspecified Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1031-1043, September.
    25. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    26. Kevin M. Quinn & Burt L. Monroe & Michael Colaresi & Michael H. Crespin & Dragomir R. Radev, 2010. "How to Analyze Political Attention with Minimal Assumptions and Costs," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 209-228, January.
    27. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    28. Albert Saiz & Uri Simonsohn, 2013. "Proxying For Unobservable Variables With Internet Document-Frequency," Journal of the European Economic Association, European Economic Association, vol. 11(1), pages 137-165, February.
    29. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    30. Margaret Roberts & Brandon Stewart & Tingley, Dustin & Edoardo Airoldi, 2013. "The structural topic model and applied social science," Working Paper 132666, Harvard University OpenScholar.
    31. Wisniewski, Tomasz Piotr & Lambe, Brendan, 2013. "The role of media in the credit crunch: The case of the banking sector," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 163-175.
    32. Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
    33. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    34. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    35. Benjamin Born & Michael Ehrmann & Marcel Fratzscher, 2014. "Central Bank Communication on Financial Stability," Economic Journal, Royal Economic Society, vol. 124(577), pages 701-734, June.
    36. Joseph E. Engelberg & Christopher A. Parsons, 2011. "The Causal Impact of Media in Financial Markets," Journal of Finance, American Finance Association, vol. 66(1), pages 67-97, February.
    37. Matthew Gentzkow & Jesse Shapiro & Matt Taddy, 2016. "Measuring Polarization in High-Dimensional Data: Method and Application to Congressional Speech," Working Papers id:11114, eSocialSciences.
    38. repec:fth:prinin:455 is not listed on IDEAS
    39. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    40. Werner Antweiler & Murray Z. Frank, 2004. "Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards," Journal of Finance, American Finance Association, vol. 59(3), pages 1259-1294, June.
    41. Joshua Angrist & Alan Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Working Papers 834, Princeton University, Department of Economics, Industrial Relations Section..
    42. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    43. Matt Taddy & Matt Gardner & Liyun Chen & David Draper, 2016. "A Nonparametric Bayesian Analysis of Heterogenous Treatment Effects in Digital Experimentation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 661-672, October.
    44. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    45. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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