Ensemble Maximum Entropy Classification and Linear Regression for Author Age Prediction

TL;DR

This paper introduces an ensemble approach combining maximum entropy classification and linear regression to accurately predict author age from textual data, with applications in security, forensics, and marketing.

Contribution

It presents a novel ensemble chain method that integrates classification and regression for more precise author age prediction from text.

Findings

Improved accuracy over traditional methods

Effective handling of age as both classification and regression

Potential applications in security and marketing

Abstract

The evolution of the internet has created an abundance of unstructured data on the web, a significant part of which is textual. The task of author profiling seeks to find the demographics of people solely from their linguistic and content-based features in text. The ability to describe traits of authors clearly has applications in fields such as security and forensics, as well as marketing. Instead of seeing age as just a classification problem, we also frame age as a regression one, but use an ensemble chain method that incorporates the power of both classification and regression to learn the authors exact age.