Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification

Document Type

Book Chapter

Source of Publication

Lecture Notes in Networks and Systems

Publication Date

5-19-2023

Abstract

Author profiling is part of information retrieval in which different perspectives of the author are observed by considering various characteristics like native language, gender, and age. Different techniques are used to extract the required information using text analysis, like author identification on social media and for Short Text Message Service. Author profiling helps in security and blogs for identification purposes while capturing authors’ writing behaviors through messages, posts, comments, blogs, comments, and chat logs. Most of the work in this area has been done in English and other native languages. On the other hand, Roman Urdu is also getting attention for the author profiling task, but it needs to convert Roman-Urdu to English to extract important features like Named Entity Recognition (NER) and other linguistic features. The conversion may lose important information while having limitations in converting one language to another language. This research explores machine learning techniques that can be used for all languages to overcome the conversion limitation. The Vector Space Model (VSM) and Query Likelihood (Q.L.) are used to identify the author’s age and gender. Experimental results revealed that Q.L. produces better results in terms of accuracy.

ISBN

978-981-19-9330-5, 978-981-19-9331-2

ISSN

2367-3389

Publisher

Springer Nature Singapore

Volume

614

First Page

123

Last Page

138

Disciplines

Computer Sciences

Keywords

Vector space model, Query likelihood model, Information retrieval (I.R.), Text mining, Author profiling

Indexed in Scopus

no

Open Access

no

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