Title: A machine learning approach for celebrity profiling

Authors: Durga Prasad Kavadi; Fadi Al-Turjman; K. Adi Narayana Reddy; Rizwan Patan

Addresses: Department of Information Technology, B V Raju Institute of Technology, Narsapur, Telangana, India ' Research Center for AI and IoT, Artificial Intelligence Engineering Department, Near East University, 99138, Nicosia, Mersin 10, Turkey ' Department of Information Technology, BVRIT Hyderabad College of Engineering for Women, Bachupally, Hyderabad, Telangana, India ' Department of Computing Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, 520007, India

Abstract: The celebrity profiling is used to predict the sub-profiles like gender, fame, birth-year and occupation of a celebrity for a given textual content. The task of celebrity profiling is introduced in PAN Competition 2019. Most of the researchers in the competition have shown interest on stylistic features to differentiate the writing styles of the celebrities. In this work, a sub-profile based weighted approach is proposed to improve the accuracy of celebrity profiling. In this approach, most frequent terms are used to compute the document weight. The document weights were used to represent the document vectors instead of weights of features. The document vectors forwarded to machine learning algorithms to build the training model. The proposed method achieved competitive accuracies of 77.13% for gender prediction, 87.76% for fame prediction and 91.54% for occupation prediction. The accuracies of the proposed approach for sub-profiles prediction outperform several existing approaches for celebrity profiling.

Keywords: celebrity profiling; author profiling; gender prediction; fame prediction; occupation prediction; machine learning algorithms; accuracy.

DOI: 10.1504/IJAHUC.2021.119091

International Journal of Ad Hoc and Ubiquitous Computing, 2021 Vol.38 No.1/2/3, pp.111 - 126

Received: 13 Sep 2020
Accepted: 09 Dec 2020
Published online: 22 Nov 2021 *

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