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Gender Profiling from PhD Theses Using k-Nearest Neighbour and Sequential Minimal Optimisation

  • Hoshiladevi Ramnial
  • Shireen Panchoo
  • Sameerchand Pudaruth
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 385)

Abstract

Author profiling is a subfield of text categorisation in which the aim is to predict some characteristics of a writer. In this paper, our objective is to determine the gender of an author based on their writings. Our corpus consists of 10 PhD theses which was split into equal sized segments of 1000, 5000 and 10000 words. From this corpus, a total of 446 features were extracted. Some new features like combined-words, new words endings and new POS tags were used in this study. The features were not separated into categories. Two machine learning classifiers, namely the k-nearest neighbour and a support vector machines classifier were used to assess the practicability and utility of our study. We were able to achieve 100% accuracy using the sequential minimal optimisation (SMO) algorithm with 40 document parts. Surprisingly, the simple and lazy k-nearest neighbour (kNN) classifier which is often discarded in gender profiling studies achieved a 98% accuracy with the same group of documents. Furthermore, 5-NN and 7-NN even outperformed SMO when using 400 document parts of 1000 words each. These values are much higher than those obtained in previous studies. However, we have used a new dataset and the results are therefore not directly comparable. Thus, our experiments provide further evidence that it is possible to infer the gender of an author using a computational linguistic approach.

Keywords

Gender profiling Text classification Machine learning 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hoshiladevi Ramnial
    • 1
  • Shireen Panchoo
    • 1
  • Sameerchand Pudaruth
    • 2
  1. 1.School of Innovative Technologies and EngineeringUniversity of TechnologyPort LouisMauritius
  2. 2.Department of Ocean Engineering and ICT, Faculty of Ocean StudiesUniversity of MauritiusPort LouisMauritius

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