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

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.

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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.

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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