Age Identification of Twitter Users: Classification Methods and Sociolinguistic Analysis

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceeding

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.

Detaljer

Författare
Externa organisationer
  • University of Patras
  • University of Hertfordshire
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Jämförande språkvetenskap och lingvistik

Nyckelord

Originalspråkengelska
Titel på värdpublikationComputational Linguistics and Intelligent Text Processing
Undertitel på gästpublikation17th International Conference, CICLing 2016, Konya, Turkey, April 3–9, 2016, Revised Selected Papers, Part II
RedaktörerAlexander Gelbukh
UtgivningsortCham
FörlagSpringer
Sidor385-395
ISBN (elektroniskt)978-3-319-75487-1
ISBN (tryckt)978-3-319-75486-4
StatusPublished - 2018
PublikationskategoriForskning
Peer review utfördJa
Externt publiceradJa
Evenemang17th International Conference on Intelligent Text Processing and Computational Linguistics: CICLing 2016 - Konya, Turkiet
Varaktighet: 2016 apr 32016 apr 9
http://www.cicling.org/2016/

Publikationsserier

NamnLecture Notes in Computer Science (LNCS)
Volym9624

Konferens

Konferens17th International Conference on Intelligent Text Processing and Computational Linguistics
LandTurkiet
OrtKonya
Period2016/04/032016/04/09
Internetadress