Gender Classification of Web Authors Using Feature Selection and Language Models

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Abstract

In the present article, we address the problem of automatic gender classification of web blog authors. More specifically, we employ eight widely used machine learning algorithms, in order to study the effectiveness of feature selection on improving the accuracy of gender classification. The feature ranking is performed over a set of statistical, part-of-speech tagging and language model features. In the experiments, we employed classification models based on decision trees, support vector machines and lazy-learning algorithms. The experimental evaluation performed on blog author gender classification data demonstrated the importance of language model features for this task and that feature selection significantly improves the accuracy of gender classification, regardless of the type of the machine learning algorithm used.

Details

Authors
External organisations
  • University of Patras
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • General Language Studies and Linguistics

Keywords

  • Text classification , Gender identification, Feature selection
Original languageEnglish
Title of host publicationSpeech and Computer
Subtitle of host publication17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings
EditorsAndrey Ronzhin, Rodmonga Potapova, Nikos Fakotakis
PublisherSpringer
Pages226-233
ISBN (Electronic)978-3-319-23132-7
ISBN (Print)978-3-319-23131-0
Publication statusPublished - 2015
Publication categoryResearch
Peer-reviewedYes
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
Volume9319
ISSN (Electronic)0302-9743