Sentiment translation for low resourced
languages: experiments on Irish general election
Tweets
Afli, HaithemORCID: 0000-0002-7449-4707, Maguire, Sorcha and Way, AndyORCID: 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.
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