ABMRF:An Ensemble Model for Author Profiling Based on Stylistic Features Using Roman Urdu  

作　　者：Aiman Muhammad Arshad Bilal Khan Khalil Khan Ali Mustafa Qamar Rehan Ullah Khan 

机构地区：[1]Department of Computer Science,City University of Science and Information Technology,Peshawar,Pakistan [2]Department of Computer Science,School of Engineering and Digital Sciences,Nazarbayev University,Astana,Kazakhstan [3]Department of Computer Science,College of Computer,Qassim University,Buraydah,Saudi Arabia [4]Department of Information Technology,College of Computer,Qassim University,Buraydah,Saudi Arabia

出　　处：《Intelligent Automation & Soft Computing》2024年第2期301-317,共17页智能自动化与软计算（英文）

摘　　要：This study explores the area of Author Profiling(AP)and its importance in several industries,including forensics,security,marketing,and education.A key component of AP is the extraction of useful information from text,with an emphasis on the writers’ages and genders.To improve the accuracy of AP tasks,the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1(ABM1)and Random Forest(RF).The work uses an extensive technique that involves textmessage dataset pretreatment,model training,and assessment.To evaluate the effectiveness of several machine learning(ML)algorithms in classifying age and gender,including Composite Hypercube on Random Projection(CHIRP),Decision Trees(J48),Na飗e Bayes(NB),K Nearest Neighbor,AdaboostM1,NB-Updatable,RF,andABMRF,they are compared.The findings demonstrate thatABMRFregularly beats the competition,with a gender classification accuracy of 71.14%and an age classification accuracy of 54.29%,respectively.Additional metrics like precision,recall,F-measure,Matthews Correlation Coefficient(MCC),and accuracy support ABMRF’s outstanding performance in age and gender profiling tasks.This study demonstrates the usefulness of ABMRF as an ensemble model for author profiling and highlights its possible uses in marketing,law enforcement,and education.The results emphasize the effectiveness of ensemble approaches in enhancing author profiling task accuracy,particularly when it comes to age and gender identification.

关 键 词：Machine learning author profiling AdaBoostM1 random forest ensemble learning text classification 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]