Automatic Estimation of Web Bloggers’ Age Using Regression Models

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


In this article, we address the problem of automatic age estimation of web users based on their posts. Most studies on age identification treat the issue as a classification problem. Instead of following an age category classification approach, we investigate the appropriateness of several regression algorithms on the task of age estimation. We evaluate a number of well-known and widely used machine learning algorithms for numerical estimation, in order to examine their appropriateness on this task. We used a set of 42 text features. The experimental results showed that the Bagging algorithm with RepTree base learner offered the best performance, achieving estimation of web users’ age with mean absolute error equal to 5.44, while the root mean squared error is approximately 7.14.


External organisations
  • University of Patras
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • General Language Studies and Linguistics


  • Author’s age estimation, Text processing, Regression algorithms
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
ISBN (Electronic)978-3-319-23132-7
Publication statusPublished - 2015
Publication categoryResearch
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743