@inproceedings{scheible-schutze-2012-bootstrapping,
title = "Bootstrapping Sentiment Labels For Unannotated Documents With Polarity {P}age{R}ank",
author = {Scheible, Christian and
Sch{\"u}tze, Hinrich},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/124_Paper.pdf",
pages = "1230--1234",
abstract = "We present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is propagated from an initial seed lexicon through a joint graph representation of words and documents. We report improved classification accuracies across multiple domains for the base models and the maximum entropy model bootstrapped from the PageRank annotation.",
}
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<abstract>We present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is propagated from an initial seed lexicon through a joint graph representation of words and documents. We report improved classification accuracies across multiple domains for the base models and the maximum entropy model bootstrapped from the PageRank annotation.</abstract>
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%0 Conference Proceedings
%T Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank
%A Scheible, Christian
%A Schütze, Hinrich
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Doğan, Mehmet Uğur
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 May
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F scheible-schutze-2012-bootstrapping
%X We present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is propagated from an initial seed lexicon through a joint graph representation of words and documents. We report improved classification accuracies across multiple domains for the base models and the maximum entropy model bootstrapped from the PageRank annotation.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/124_Paper.pdf
%P 1230-1234
Markdown (Informal)
[Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank](http://www.lrec-conf.org/proceedings/lrec2012/pdf/124_Paper.pdf) (Scheible & Schütze, LREC 2012)
ACL