Gender and Age Prediction Multilingual Author Profiles Based on Comments

Ali Nemati
2018 Forum for Information Retrieval Evaluation  
Recently, several approaching been presented to detect automatically users' age and gender classification from multiple languages based on documents, text, and comments on the web or social media update status. The purpose of this task is determining and detecting information such as age, and gender from multilingual (Roman, Urdu and English) author profiles based on texts or documents. By using four machine learning techniques, my system derives an ensemble model for age and gender categories.
more » ... The ensemble model is composed of a multinomial Naive Bayes classifier, a Gradient Boosting Classifier, a Logistic Regression CV and a Multi-Layer Perceptron classifier. The system can categorize and diagnose text source automatically with a sensitivity and specificity of age and gender with unknown testing data. The accuracy result is 83 percent for gender category, 60 percent for age, and accuracy 49 percent is for joint age and gender category.
dblp:conf/fire/Nemati18 fatcat:vl5jk232prbslotx2ildi6zvgy