A New Term Weight Measure for Gender Prediction in Author Profiling

  • Ch. Swathi
  • K. Karunakar
  • G. Archana
  • T. Raghunadha Reddy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

Author profiling is used to predict the demographic characteristics such as gender, age, native language, location, and educational background of the authors by analyzing their writing styles. The researchers in author profiling proposed various features such as character-based, word-based, structural, syntactic, and semantic features to differentiate the writing styles of the authors. The existing approaches in author profiling used the frequency of a feature to represent the document vector. In this work, the experimented carried with various features with their frequency and observed that only frequency is not suitable to assign better discriminative power to the features. Later, a new supervised term weight measure is proposed to assign suitable weights to the terms and analyzed the accuracies with various machine learning algorithms. The experimentation carried out on review domain and the proposed supervised term weight measure obtained good accuracy for gender prediction when compared to existing approaches.

Keywords

Term weight measure Gender prediction BOW approach Author profiling 

References

  1. 1.
    Reddy, T.R., Vardhan, B.V., Reddy, P.V.: A survey on authorship profiling techniques. Int. J. Appl. Eng. Res. 11(5), 3092–3102 (2016)Google Scholar
  2. 2.
    Soler-Company, J., Wanner, L.: How to use less features and reach better performance in author gender identification. In: The 9th edition of the Language Resources and Evaluation Conference (LREC), pp. 1315–1319, May 2007Google Scholar
  3. 3.
    Argamon, S., Koppel, M., Pennebaker, J.W., Schler, J.: Automatically profiling the author of an anonymous text. Commun. ACM 52(2), 119–123 (2009)Google Scholar
  4. 4.
    Estival, D., Gaustad, T., Pham, S.B., Radford, W., Hutchinson, B.: Author profiling for english emails. In: 10th Conference of the Pacific Association for Computational Linguistics (PACLING, 2007), pp. 263–272 (2007)Google Scholar
  5. 5.
    Argamon, KM.S., Shimoni, A.: Automatically categorizing written texts by author gender. In: Literary and Linguistic Computing, pp. 401–412 (2003)Google Scholar
  6. 6.
    Schler, J., Koppel, M., Argamon, S., Pennebaker, J.: Effects of age and gender on blogging. In: Proceedings of AAAI Spring Symposium on Computational Approaches for Analyzing Weblogs, March 2006Google Scholar
  7. 7.
    Dang Duc, P., Giang Binh, T., Son Bao, P.: Authorship attribution and gender identification in greek blogs. In: 8th International Conference on Quantitative Linguistics (QUALICO), pp. 21–32, April 26–29, 2012Google Scholar
  8. 8.
    Dang Duc, P., Giang Binh, T., Son Bao, P.: Author Profiling for vietnamese blogs. In: Asian Language Processing, 2009 (IALP ’09), pp. 190–194 (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Ch. Swathi
    • 1
  • K. Karunakar
    • 2
  • G. Archana
    • 3
  • T. Raghunadha Reddy
    • 4
  1. 1.Department of CSEG Narayanamma Institute of Technology and ScienceHyderabadIndia
  2. 2.Department of CSESwarnandhra Institute of Engineering and TechnologyNarsapurIndia
  3. 3.Department of CSESwarnandhra College of Engineering and TechnologyNarsapurIndia
  4. 4.Department of ITVardhaman College of EngineeringHyderabadIndia

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