A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization
Based on Minimum Cuts
B. Pang, and L. Lee. Proceedings of the Association for Computational Linguistics (ACL), page 271--278. 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 Pang+Lee:04a
%A Pang, Bo
%A Lee, Lillian
%B Proceedings of the Association for Computational Linguistics (ACL)
%D 2004
%I Association for Computational Linguistics
%K dictionary-based sentiment-analysis sentiwordnet
%P 271--278
%T A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization
Based on Minimum Cuts
%U http://portal.acm.org/citation.cfm?id=1218990
%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{Pang+Lee:04a,
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 = {2020-10-20T11:09:18.000+0200},
author = {Pang, Bo and Lee, Lillian},
biburl = {https://www.bibsonomy.org/bibtex/2676df145fce6afac5ef882e3f14b77e1/ghagerer},
booktitle = {Proceedings of the Association for Computational Linguistics (ACL)},
interhash = {bdbece23b14cf5689242ba3b6a77408f},
intrahash = {676df145fce6afac5ef882e3f14b77e1},
keywords = {dictionary-based sentiment-analysis sentiwordnet},
pages = {271--278},
publisher = {Association for Computational Linguistics},
timestamp = {2020-10-20T11:10:32.000+0200},
title = {A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization
Based on Minimum Cuts},
url = {http://portal.acm.org/citation.cfm?id=1218990},
year = 2004
}