Conference Proceedings


Andy Way
Sorcha Maguire
Haithem Afli



user generated content less resourced languages sentiment analysis irish language social media general election sentiment translation machine translating

Sentiment translation for low resourced languages: experiments on Irish general election Tweets (2017)

Abstract This paper presents two main methods of Sentiment Analysis (SA) of User-Generated Content for a low-resource language: Irish. The first method, automatic sentiment translation, applies existing English SA resources to both manually- and automatically-translated tweets. We obtained an accuracy of 70% using this approach. The second method involved the manual creation of an Irish-language sentiment lexicon: SentiFocl├│ir. This lexicon was used to build the first Irish SA system, SentiFocalTweet, which produced superior results to the first method, with an accuracy of 76%. This demonstrates that translation from Irish to English has a minor effect on the preservation of sentiment; it is also shown that the SentiFocalTweet system is a successful baseline system for Irish sentiment analysis.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: ADAPT
Ireland -> Dublin City University -> Status = Unpublished
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating

Full list of authors on original publication

Andy Way, Sorcha Maguire, Haithem Afli

Experts in our system

Andy Way
Dublin City University
Total Publications: 229
Haithem Afli
Dublin City University
Total Publications: 14