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Computer‐Assisted Keyword and Document Set Discovery from Unstructured Text

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  • Gary King
  • Patrick Lam
  • Margaret E. Roberts

Abstract

The (unheralded) first step in many applications of automated text analysis involves selecting keywords to choose documents from a large text corpus for further study. Although all substantive results depend on this choice, researchers usually pick keywords in ad hoc ways that are far from optimal and usually biased. Most seem to think that keyword selection is easy, since they do Google searches every day, but we demonstrate that humans perform exceedingly poorly at this basic task. We offer a better approach, one that also can help with following conversations where participants rapidly innovate language to evade authorities, seek political advantage, or express creativity; generic web searching; eDiscovery; look‐alike modeling; industry and intelligence analysis; and sentiment and topic analysis. We develop a computer‐assisted (as opposed to fully automated or human‐only) statistical approach that suggests keywords from available text without needing structured data as inputs. This framing poses the statistical problem in a new way, which leads to a widely applicable algorithm. Our specific approach is based on training classifiers, extracting information from (rather than correcting) their mistakes, and summarizing results with easy‐to‐understand Boolean search strings. We illustrate how the technique works with analyses of English texts about the Boston Marathon bombings, Chinese social media posts designed to evade censorship, and others.

Suggested Citation

  • Gary King & Patrick Lam & Margaret E. Roberts, 2017. "Computer‐Assisted Keyword and Document Set Discovery from Unstructured Text," American Journal of Political Science, John Wiley & Sons, vol. 61(4), pages 971-988, October.
  • Handle: RePEc:wly:amposc:v:61:y:2017:i:4:p:971-988
    DOI: 10.1111/ajps.12291
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    Citations

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    Cited by:

    1. Sandra Wankmüller, 2023. "A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis," Journal of Computational Social Science, Springer, vol. 6(1), pages 91-163, April.
    2. McCannon, Bryan & Zhou, Yang & Hall, Joshua, 2021. "Measuring a Contract’s Breadth: A Text Analysis," Working Papers 11013, George Mason University, Mercatus Center.
    3. Fraccaroli, Nicolò & Giovannini, Alessandro & Jamet, Jean-François & Persson, Eric, 2022. "Ideology and monetary policy. The role of political parties’ stances in the European Central Bank’s parliamentary hearings," European Journal of Political Economy, Elsevier, vol. 74(C).
    4. Natalie Kyung Won Kim & Sera Choi & Taejin Jung & Sohee Park, 2023. "How does demand uncertainty from climate change exposure affect the firms' cost structures? Examining the real effects of climate change on the firms' operational decisions," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 30(6), pages 2969-2989, November.
    5. Callan Windsor & Max Zang, 2023. "Firms' Price-setting Behaviour: Insights from Earnings Calls," RBA Research Discussion Papers rdp2023-06, Reserve Bank of Australia.
    6. Annie Collins & Rohan Alexander, 2022. "Reproducibility of COVID-19 pre-prints," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4655-4673, August.
    7. Fraccaroli, Nicolò & Giovannini, Alessandro & Jamet, Jean-Francois & Persson, Eric, 2022. "Ideology and monetary policy: the role of political parties’ stances in the ECB’s parliamentary hearings," Working Paper Series 2655, European Central Bank.
    8. Erkan Işığıçok & Sadullah Çelik & Dilek Özdemir Yılmaz, 2023. "Analysis of Skills and Qualifications Required in Data Scientist Job Postings Based on the Pareto Analysis Perspective Using Text Mining," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 10-25, December.
    9. Sophie E. Jordan & Sierra E. Hovet & Isaac Chun-Hai Fung & Hai Liang & King-Wa Fu & Zion Tsz Ho Tse, 2018. "Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response," Data, MDPI, vol. 4(1), pages 1-20, December.
    10. Gostlow, Glen, 2020. "The materiality and measurement of physical climate risk: evidence from Form 8-K," LSE Research Online Documents on Economics 107045, London School of Economics and Political Science, LSE Library.
    11. Yu-Ru Lin & Wen-Ting Chung, 2020. "The dynamics of Twitter users’ gun narratives across major mass shooting events," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-16, December.
    12. Zhang, Si Ying, 2022. "Are investors sensitive to climate-related transition and physical risks? Evidence from global stock markets," Research in International Business and Finance, Elsevier, vol. 62(C).
    13. Crocker H. Liu & Adam Nowak & Patrick S. Smith, 2018. "Does the Asset Pricing Premium Reflect Asymmetric or Incomplete Information?," Working Papers 18-06, Department of Economics, West Virginia University.

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