@inproceedings{cieliebak-etal-2014-meta,
title = "Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools",
author = {Cieliebak, Mark and
D{\"u}rr, Oliver and
Uzdilli, Fatih},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/820_Paper.pdf",
pages = "3100--3104",
abstract = "In this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, reviews etc. The best commercial tools have average accuracy of 60{\%}. We then apply machine learning techniques (Random Forests) to combine all tools, and show that this results in a meta-classifier that improves the overall performance significantly.",
}
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%0 Conference Proceedings
%T Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools
%A Cieliebak, Mark
%A Dürr, Oliver
%A Uzdilli, Fatih
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F cieliebak-etal-2014-meta
%X In this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, reviews etc. The best commercial tools have average accuracy of 60%. We then apply machine learning techniques (Random Forests) to combine all tools, and show that this results in a meta-classifier that improves the overall performance significantly.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/820_Paper.pdf
%P 3100-3104
Markdown (Informal)
[Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools](http://www.lrec-conf.org/proceedings/lrec2014/pdf/820_Paper.pdf) (Cieliebak et al., LREC 2014)
ACL