A Sentimental Education: Sentiment Analysis using
Subjectivity Summarization based on Minimum Cuts
B. Pang, and L. Lee. Proceedings of ACL-04, 42nd Meeting of the Association
for Computational Linguistics, page 271--278. Barcelona, ES, Association for Computational Linguistics, (2004)
Abstract
Sentiment analysis seeks to identify the viewpoint(s)
underlying a text span; an example application is
classifying a movie review as ``thumbs up'' or ``thumbs
down''. To determine this sentiment polarity, we
propose a novel machine-learning method that applies
text-categorization techniques to just the subjective
portions of the document. Extracting these portions can
be implemented using efficient techniques for finding
minimum cuts in graphs; this greatly facilitates
incorporation of cross-sentence contextual
constraints.
%0 Conference Paper
%1 Pang04
%A Pang, Bo
%A Lee, Lillian
%B Proceedings of ACL-04, 42nd Meeting of the Association
for Computational Linguistics
%C Barcelona, ES
%D 2004
%I Association for Computational Linguistics
%K imported
%P 271--278
%T A Sentimental Education: Sentiment Analysis using
Subjectivity Summarization based on Minimum Cuts
%U http://www.cs.cornell.edu/home/llee/papers/cutsent.pdf
%X Sentiment analysis seeks to identify the viewpoint(s)
underlying a text span; an example application is
classifying a movie review as ``thumbs up'' or ``thumbs
down''. To determine this sentiment polarity, we
propose a novel machine-learning method that applies
text-categorization techniques to just the subjective
portions of the document. Extracting these portions can
be implemented using efficient techniques for finding
minimum cuts in graphs; this greatly facilitates
incorporation of cross-sentence contextual
constraints.
@inproceedings{Pang04,
abstract = {Sentiment analysis seeks to identify the viewpoint(s)
underlying a text span; an example application is
classifying a movie review as ``thumbs up'' or ``thumbs
down''. To determine this sentiment polarity, we
propose a novel machine-learning method that applies
text-categorization techniques to just the subjective
portions of the document. Extracting these portions can
be implemented using efficient techniques for finding
minimum cuts in graphs; this greatly facilitates
incorporation of cross-sentence contextual
constraints.},
added-at = {2009-01-22T05:56:16.000+0100},
address = {Barcelona, ES},
author = {Pang, Bo and Lee, Lillian},
biburl = {https://www.bibsonomy.org/bibtex/26c28e5e51c7bc97e3d8be15461dfb8a9/kabloom},
booktitle = {Proceedings of ACL-04, 42nd Meeting of the Association
for Computational Linguistics},
interhash = {bdbece23b14cf5689242ba3b6a77408f},
intrahash = {6c28e5e51c7bc97e3d8be15461dfb8a9},
keywords = {imported},
pages = {271--278},
pdf = {Pang04.pdf},
publisher = {Association for Computational Linguistics},
timestamp = {2011-03-09T04:37:11.000+0100},
title = {A Sentimental Education: {S}entiment Analysis using
Subjectivity Summarization based on Minimum Cuts},
url = {http://www.cs.cornell.edu/home/llee/papers/cutsent.pdf},
year = 2004
}