TY - GEN
T1 - Automatic estimation of web bloggers’ age using regression models
AU - Simaki, Vasiliki
AU - Aravantinou, Christina
AU - Mporas, Iosif
AU - Megalooikonomou, Vasileios
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - Author’s age estimation
KW - Regression algorithms
KW - Text processing
UR - http://www.scopus.com/inward/record.url?scp=84945961208&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23132-7_14
DO - 10.1007/978-3-319-23132-7_14
M3 - Conference contribution
AN - SCOPUS:84945961208
SN - 9783319231310
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 113
EP - 120
BT - Speech and Computer - 17th International Conference, SPECOM 2015, Proceedings
A2 - Ronzhin, Andrey
A2 - Potapova, Rodmonga
A2 - Fakotakis, Nikos
PB - Springer Nature
T2 - 17th International Conference on Speech and Computer, SPECOM 2015
Y2 - 20 September 2015 through 24 September 2015
ER -