Contextually Propagated Term Weight based Approach for Author Profiling: Gender, Age and Language Variety Prediction

Abstract

Author profiling creates characterization of an author based on attributes such as age, gender, language, dialect region variety, personality and so on. In recent years it has garnered significant attention for its varies applications across forensic linguistics, marketing, cybersecurity and social media analytics. Most of the research focused on stylistic, content based and term weight measures based feature representations.  We observed that context semantics is not considered in the feature representation. In this propose contextually propagated term weight measures for feature representation. We implemented SVM, Random Forest and XG Boost machine learning algorithms on those feature representations. The results demonstrated that the proposed contextually propagated term weight with inverse category frequency outperformed the existing methods.