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Text mining with n-gram variables

Author

Listed:
  • Matthias Schonlau

    (University of Waterloo)

  • Nick Guenther

    (University of Waterloo)

  • Ilia Sucholutsky

    (University of Waterloo)

Abstract

Text mining is the process of turning free text into numerical variables and then analyzing them with statistical techniques. We introduce the command ngram, which implements the most common approach to text mining, the “bag of words”. An n-gram is a contiguous sequence of words in a text. Broadly speaking, ngram creates hundreds or thousands of variables, each recording how often the corresponding n-gram occurs in a given text. This is more useful than it sounds. We illustrate ngram with the categorization of text answers from two open-ended questions. Copyright 2016 by StataCorp LP.

Suggested Citation

  • Matthias Schonlau & Nick Guenther & Ilia Sucholutsky, 2017. "Text mining with n-gram variables," Stata Journal, StataCorp LP, vol. 17(4), pages 866-881, December.
    • Handle: RePEc:tsj:stataj:v:17:y:2017:i:4:p:866-881
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    Cited by:

    1. Brown, Martin & Schmitz, Jan & Zehnder, Christian, 2024. "Communication and hidden action: A credit market experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 423-455.
    2. Muhammed-Fatih Kaya, 2022. "Pattern Labelling of Business Communication Data," Group Decision and Negotiation, Springer, vol. 31(6), pages 1203-1234, December.
    3. Cristina Cattaneo & Daniela Grieco & Nicola Lacetera & Mario Macis, 2024. "Out-group Penalties in Refugee Assistance: A Survey Experiment," NBER Working Papers 32139, National Bureau of Economic Research, Inc.
    4. Paul M. Anglin & Yanmin Gao, 2023. "Value of Communication and Social Media: An Equilibrium Theory of Messaging," The Journal of Real Estate Finance and Economics, Springer, vol. 66(4), pages 861-903, May.
    5. Fatma Yiğit Açikgöz & Mehmet Kayakuş & Georgiana Moiceanu & Nesrin Sönmez, 2024. "A New Approach to Assess Sustainable Corporate Reputation with Citizen Comments Using Machine Learning and Natural Language Processing," Sustainability, MDPI, vol. 16(22), pages 1-19, November.
    6. Christopher Haynes & Marco A. Palomino & Liz Stuart & David Viira & Frances Hannon & Gemma Crossingham & Kate Tantam, 2022. "Automatic Classification of National Health Service Feedback," Mathematics, MDPI, vol. 10(6), pages 1-23, March.

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