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The textcat Package for n-Gram Based Text Categorization in R

Author

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  • Hornik, Kurt
  • Mair, Patrick
  • Rauch, Johannes
  • Geiger, Wilhelm
  • Buchta, Christian
  • Feinerer, Ingo

Abstract

Identifying the language used will typically be the first step in most natural language processing tasks. Among the wide variety of language identification methods discussed in the literature, the ones employing the Cavnar and Trenkle (1994) approach to text categorization based on character n-gram frequencies have been particularly successful. This paper presents the R extension package textcat for n-gram based text categorization which implements both the Cavnar and Trenkle approach as well as a reduced n-gram approach designed to remove redundancies of the original approach. A multi-lingual corpus obtained from the Wikipedia pages available on a selection of topics is used to illustrate the functionality of the package and the performance of the provided language identification methods.

Suggested Citation

  • Hornik, Kurt & Mair, Patrick & Rauch, Johannes & Geiger, Wilhelm & Buchta, Christian & Feinerer, Ingo, 2013. "The textcat Package for n-Gram Based Text Categorization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i06).
    • Handle: RePEc:jss:jstsof:v:052:i06
    DOI: http://hdl.handle.net/10.18637/jss.v052.i06
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    References listed on IDEAS

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    1. Khreisat, Laila, 2009. "A machine learning approach for Arabic text classification using N-gram frequency statistics," Journal of Informetrics, Elsevier, vol. 3(1), pages 72-77.
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    1. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    2. Alessandra Garbero & Giuliano Resce & Bia Carneiro, 2021. "Spatial dynamics across food systems transformation in IFAD investments: a machine learning approach," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(5), pages 1125-1143, October.
    3. Lawani, Abdelaziz & Reed, Michael R. & Mark, Tyler & Zheng, Yuqing, 2019. "Reviews and price on online platforms: Evidence from sentiment analysis of Airbnb reviews in Boston," Regional Science and Urban Economics, Elsevier, vol. 75(C), pages 22-34.
    4. Liu, Yong & Teichert, Thorsten & Rossi, Matti & Li, Hongxiu & Hu, Feng, 2017. "Big data for big insights: Investigating language-specific drivers of hotel satisfaction with 412,784 user-generated reviews," Tourism Management, Elsevier, vol. 59(C), pages 554-563.
    5. Garbero, Alessandra & Carneiro, Bia & Resce, Giuliano, 2021. "Harnessing the power of machine learning analytics to understand food systems dynamics across development projects," Technological Forecasting and Social Change, Elsevier, vol. 172(C).

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