University of Hertfordshire

By the same authors

Age identification of twitter users: Classification methods and sociolinguistic analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publicationComputational Linguistics and Intelligent Text Processing - 17th International Conference, CICLing 2016, Revised Selected Papers
EditorsAlexander Gelbukh
PublisherSpringer Verlag
Pages385-395
Number of pages11
ISBN (Print)9783319754864
DOIs
Publication statusPublished - 1 Jan 2018
Event17th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2016 - Konya, Turkey
Duration: 3 Apr 20169 Apr 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9624 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2016
CountryTurkey
CityKonya
Period3/04/169/04/16

Abstract

In this article, we address the problem of age identification of Twitter users, after their online text. We used a set of text mining, sociolinguistic-based and content-related text features, and we evaluated a number of well-known and widely used machine learning algorithms for classification, in order to examine their appropriateness on this task. The experimental results showed that Random Forest algorithm offered superior performance achieving accuracy equal to 61%. We ranked the classification features after their informativity, using the ReliefF algorithm, and we analyzed the results in terms of the sociolinguistic principles on age linguistic variation.

ID: 19522186