A Document Weighted Approach for Gender and Age Prediction Based on Term Weight Measure

Author profiling is a text classification technique, which is used to predict the profiles of unknown text by analyzing their writing styles. Author profiles are the characteristics of the authors like gender, age, nativity language, country and educational background. The existing approaches for Author Profiling suffered from problems like high dimensionality of features and fail to capture the relationship between the features. In this work, a new document weighted approach is proposed in order to address the problems in existing approaches. In this approach, the term weight measure is used to assign suitable weight to the terms and these term weights are aggregated to compute the document weight. The classification model is generated with these document weights for predicting profiles of the text. The proposed approach and existing approaches are experimented on reviews domain with different classifiers. The accuracies of the proposed approach for gender and age prediction are promising than existing approaches.