Afli, Haithem ORCID: 0000-0002-7449-4707, Maguire, Sorcha and Way, Andy ORCID: 0000-0001-5736-5930 (2017) Sentiment translation for low resourced languages: experiments on Irish general election Tweets. In: 18th International Conference on Computational Linguistics and Intelligent Text Processing, 17-21 Apr 2017, Budapest, Hungry.
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: SentiFoclóir. 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.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Uncontrolled Keywords: | User-Generated Content; Social Media; Less-resourced languages; Sentiment translation |
Subjects: | Computer Science > Machine translating |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing Research Initiatives and Centres > ADAPT |
Copyright Information: | © 2017 The Authors |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
ID Code: | 23370 |
Deposited On: | 28 May 2019 14:43 by Thomas Murtagh . Last Modified 28 May 2019 14:43 |
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