@inproceedings{kokkinakis-etal-2023-scaling,
title = "Scaling-up the Resources for a Freely Available {S}wedish {VADER} (sv{VADER})",
author = "Kokkinakis, Dimitrios and
Mu{\~n}oz S{\'a}nchez, Ricardo and
Hammarlin, Mia-Marie",
editor = {Alum{\"a}e, Tanel and
Fishel, Mark},
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.66",
pages = "667--672",
abstract = "With widespread commercial applications in various domains, sentiment analysis has become a success story for Natural Language Processing (NLP). Still, although sentiment analysis has rapidly progressed during the last years, mainly due to the application of modern AI technologies, many approaches apply knowledge-based strategies, such as lexicon-based, to the task. This is particularly true for analyzing short social media content, e.g., tweets. Moreover, lexicon-based sentiment analysis approaches are usually preferred over learning-based methods when training data is unavailable or insufficient. Therefore, our main goal is to scale-up and apply a lexicon-based approach which can be used as a baseline to Swedish sentiment analysis. All scaled-up resources are made available, while the performance of this enhanced tool is evaluated on two short datasets, achieving adequate results.",
}
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%0 Conference Proceedings
%T Scaling-up the Resources for a Freely Available Swedish VADER (svVADER)
%A Kokkinakis, Dimitrios
%A Muñoz Sánchez, Ricardo
%A Hammarlin, Mia-Marie
%Y Alumäe, Tanel
%Y Fishel, Mark
%S Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2023
%8 May
%I University of Tartu Library
%C Tórshavn, Faroe Islands
%F kokkinakis-etal-2023-scaling
%X With widespread commercial applications in various domains, sentiment analysis has become a success story for Natural Language Processing (NLP). Still, although sentiment analysis has rapidly progressed during the last years, mainly due to the application of modern AI technologies, many approaches apply knowledge-based strategies, such as lexicon-based, to the task. This is particularly true for analyzing short social media content, e.g., tweets. Moreover, lexicon-based sentiment analysis approaches are usually preferred over learning-based methods when training data is unavailable or insufficient. Therefore, our main goal is to scale-up and apply a lexicon-based approach which can be used as a baseline to Swedish sentiment analysis. All scaled-up resources are made available, while the performance of this enhanced tool is evaluated on two short datasets, achieving adequate results.
%U https://aclanthology.org/2023.nodalida-1.66
%P 667-672
Markdown (Informal)
[Scaling-up the Resources for a Freely Available Swedish VADER (svVADER)](https://aclanthology.org/2023.nodalida-1.66) (Kokkinakis et al., NoDaLiDa 2023)
ACL