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The influence of preprocessing on text classification using a bag-of-words representation

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  • Yaakov HaCohen-Kerner
  • Daniel Miller
  • Yair Yigal

Abstract

Text classification (TC) is the task of automatically assigning documents to a fixed number of categories. TC is an important component in many text applications. Many of these applications perform preprocessing. There are different types of text preprocessing, e.g., conversion of uppercase letters into lowercase letters, HTML tag removal, stopword removal, punctuation mark removal, lemmatization, correction of common misspelled words, and reduction of replicated characters. We hypothesize that the application of different combinations of preprocessing methods can improve TC results. Therefore, we performed an extensive and systematic set of TC experiments (and this is our main research contribution) to explore the impact of all possible combinations of five/six basic preprocessing methods on four benchmark text corpora (and not samples of them) using three ML methods and training and test sets. The general conclusion (at least for the datasets verified) is that it is always advisable to perform an extensive and systematic variety of preprocessing methods combined with TC experiments because it contributes to improve TC accuracy. For all the tested datasets, there was always at least one combination of basic preprocessing methods that could be recommended to significantly improve the TC using a BOW representation. For three datasets, stopword removal was the only single preprocessing method that enabled a significant improvement compared to the baseline result using a bag of 1,000-word unigrams. For some of the datasets, there was minimal improvement when we removed HTML tags, performed spelling correction or removed punctuation marks, and reduced replicated characters. However, for the fourth dataset, the stopword removal was not beneficial. Instead, the conversion of uppercase letters into lowercase letters was the only single preprocessing method that demonstrated a significant improvement compared to the baseline result. The best result for this dataset was obtained when we performed spelling correction and conversion into lowercase letters. In general, for all the datasets processed, there was always at least one combination of basic preprocessing methods that could be recommended to improve the accuracy results when using a bag-of-words representation.

Suggested Citation

  • Yaakov HaCohen-Kerner & Daniel Miller & Yair Yigal, 2020. "The influence of preprocessing on text classification using a bag-of-words representation," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-22, May.
  • Handle: RePEc:plo:pone00:0232525
    DOI: 10.1371/journal.pone.0232525
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    References listed on IDEAS

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    1. Shlomo Argamon & Casey Whitelaw & Paul Chase & Sobhan Raj Hota & Navendu Garg & Shlomo Levitan, 2007. "Stylistic text classification using functional lexical features," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(6), pages 802-822, April.
    2. Yaakov HaCohen‐Kerner & Hananya Beck & Elchai Yehudai & Mordechay Rosenstein & Dror Mughaz, 2010. "Cuisine: Classification using stylistic feature sets and/or name‐based feature sets," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(8), pages 1644-1657, August.
    3. Yaakov HaCohen-Kerner & Hananya Beck & Elchai Yehudai & Mordechay Rosenstein & Dror Mughaz, 2010. "Cuisine: Classification using stylistic feature sets and/or name-based feature sets," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(8), pages 1644-1657, August.
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    1. Xu, Qianwen Ariel & Jayne, Chrisina & Chang, Victor, 2024. "An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews," Technological Forecasting and Social Change, Elsevier, vol. 202(C).

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