@inproceedings{saggion-funk-2010-interpreting,
title = "Interpreting {S}enti{W}ord{N}et for Opinion Classification",
author = "Saggion, Horacio and
Funk, Adam",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/354_Paper.pdf",
abstract = "We describe a set of tools, resources, and experiments for opinion classification in business-related datasources in two languages. In particular we concentrate on SentiWordNet text interpretation to produce word, sentence, and text-based sentiment features for opinion classification. We achieve good results in experiments using supervised learning machine over syntactic and sentiment-based features. We also show preliminary experiments where the use of summaries before opinion classification provides competitive advantage over the use of full documents.",
}
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<abstract>We describe a set of tools, resources, and experiments for opinion classification in business-related datasources in two languages. In particular we concentrate on SentiWordNet text interpretation to produce word, sentence, and text-based sentiment features for opinion classification. We achieve good results in experiments using supervised learning machine over syntactic and sentiment-based features. We also show preliminary experiments where the use of summaries before opinion classification provides competitive advantage over the use of full documents.</abstract>
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%0 Conference Proceedings
%T Interpreting SentiWordNet for Opinion Classification
%A Saggion, Horacio
%A Funk, Adam
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F saggion-funk-2010-interpreting
%X We describe a set of tools, resources, and experiments for opinion classification in business-related datasources in two languages. In particular we concentrate on SentiWordNet text interpretation to produce word, sentence, and text-based sentiment features for opinion classification. We achieve good results in experiments using supervised learning machine over syntactic and sentiment-based features. We also show preliminary experiments where the use of summaries before opinion classification provides competitive advantage over the use of full documents.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/354_Paper.pdf
Markdown (Informal)
[Interpreting SentiWordNet for Opinion Classification](http://www.lrec-conf.org/proceedings/lrec2010/pdf/354_Paper.pdf) (Saggion & Funk, LREC 2010)
ACL