Abstract
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.
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Simaki, V., Aravantinou, C., Mporas, I., Megalooikonomou, V. (2015). Automatic Estimation of Web Bloggers’ Age Using Regression Models . In: Ronzhin, A., Potapova, R., Fakotakis, N. (eds) Speech and Computer. SPECOM 2015. Lecture Notes in Computer Science(), vol 9319. Springer, Cham. https://doi.org/10.1007/978-3-319-23132-7_14
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DOI: https://doi.org/10.1007/978-3-319-23132-7_14
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