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Stylistic text classification using functional lexical features

Author

Listed:
  • Shlomo Argamon
  • Casey Whitelaw
  • Paul Chase
  • Sobhan Raj Hota
  • Navendu Garg
  • Shlomo Levitan

Abstract

Most text analysis and retrieval work to date has focused on the topic of a text; that is, what it is about. However, a text also contains much useful information in its style, or how it is written. This includes information about its author, its purpose, feelings it is meant to evoke, and more. This article develops a new type of lexical feature for use in stylistic text classification, based on taxonomies of various semantic functions of certain choice words or phrases. We demonstrate the usefulness of such features for the stylistic text classification tasks of determining author identity and nationality, the gender of literary characters, a text's sentiment (positive/negative evaluation), and the rhetorical character of scientific journal articles. We further show how the use of functional features aids in gaining insight about stylistic differences among different kinds of texts.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jamist:v:58:y:2007:i:6:p:802-822
    DOI: 10.1002/asi.20553
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    Cited by:

    1. Mini Zhu & Gang Wang & Chaoping Li & Hongjun Wang & Bin Zhang, 2023. "Artificial Intelligence Classification Model for Modern Chinese Poetry in Education," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    2. Rutherford, Brian A., 2013. "A genre-theoretic approach to financial reporting research," The British Accounting Review, Elsevier, vol. 45(4), pages 297-310.
    3. Zhang, Xi & Cheng, Yihang & Chen, Aoshuang & Lytras, Miltiadis & de Pablos, Patricia Ordóñez & Zhang, Renyu, 2022. "How rumors diffuse in the infodemic: Evidence from the healthy online social change in China," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    4. Shlomo Argamon & Jeff Dodick & Paul Chase, 2008. "Language use reflects scientific methodology: A corpus-based study of peer-reviewed journal articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(2), pages 203-238, May.
    5. 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.

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