Naslov
Discriminating between Closely Related Languages on Twitter

Autori
Ljubešić, Nikola ; Kranjčić, Denis

Izvornik
Informatica (Ljubljana) (0350-5596) 39 (2015), 1; 1-8

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
microblogging; language identification; closely related languages

Sažetak
In this paper we tackle the problem of discriminating Twitter users by the language they tweet in, taking into account very similar South-Slavic languages – Bosnian, Croatian, Montenegrin and Serbian. We apply the supervised machine learning approach by annotating a subset of 500 users from an existing Twitter collection by the language the users primarily tweet in. We show that by using a simple bag-of- words model, univariate feature selection, 320 strongest features and a standard classifier, we reach user classification accuracy of ∼98%. Annotating the whole 63, 160 users strong Twitter collection with the best performing classifier and visualizing it on a map via tweet geo-information, we produce a Twitter language map which clearly depicts the robustness of the classifier.

Izvorni jezik
Engleski

Znanstvena područja
Informacijske i komunikacijske znanosti

POVEZANOST RADA