Extracting Product Features and Opinions from
Reviews
A. Popescu, and O. Etzioni. Proceedings of HLT-EMNLP-05, the Human Language
Technology Conference/Conference on Empirical Methods
in Natural Language Processing, Vancouver, CA, (2005)
Abstract
Consumers are often forced to wade through many
on-line reviews in order to make an informed product
choice. This paper introduces OPINE, an unsupervised
informationextraction system which mines reviews in
order to build a model of important product features,
their evaluation by reviewers, and their relative
quality across products. Compared to previous work,
OPINE achieves 22\% higher precision (with only 3\%
lower recall) on the feature extraction task. OPINE’s
novel use of relaxation labeling for finding the
semantic orientation of words in context leads to
strong performance on the tasks of finding opinion
phrases and their polarity.
%0 Conference Paper
%1 Popescu05
%A Popescu, Ana-Maria
%A Etzioni, Oren
%B Proceedings of HLT-EMNLP-05, the Human Language
Technology Conference/Conference on Empirical Methods
in Natural Language Processing
%C Vancouver, CA
%D 2005
%K imported
%T Extracting Product Features and Opinions from
Reviews
%U http://www.cs.washington.edu/homes/etzioni/papers/emnlp05_opine.pdf
%X Consumers are often forced to wade through many
on-line reviews in order to make an informed product
choice. This paper introduces OPINE, an unsupervised
informationextraction system which mines reviews in
order to build a model of important product features,
their evaluation by reviewers, and their relative
quality across products. Compared to previous work,
OPINE achieves 22\% higher precision (with only 3\%
lower recall) on the feature extraction task. OPINE’s
novel use of relaxation labeling for finding the
semantic orientation of words in context leads to
strong performance on the tasks of finding opinion
phrases and their polarity.
@inproceedings{Popescu05,
abstract = {Consumers are often forced to wade through many
on-line reviews in order to make an informed product
choice. This paper introduces OPINE, an unsupervised
informationextraction system which mines reviews in
order to build a model of important product features,
their evaluation by reviewers, and their relative
quality across products. Compared to previous work,
OPINE achieves 22\% higher precision (with only 3\%
lower recall) on the feature extraction task. OPINE’s
novel use of relaxation labeling for finding the
semantic orientation of words in context leads to
strong performance on the tasks of finding opinion
phrases and their polarity.},
added-at = {2009-01-22T05:56:16.000+0100},
address = {Vancouver, CA},
author = {Popescu, Ana-Maria and Etzioni, Oren},
biburl = {https://www.bibsonomy.org/bibtex/2456a47f214269c02702cc12397814e91/kabloom},
booktitle = {Proceedings of HLT-EMNLP-05, the Human Language
Technology Conference/Conference on Empirical Methods
in Natural Language Processing},
interhash = {27106c540e455c2116b3182f8208fcf6},
intrahash = {456a47f214269c02702cc12397814e91},
keywords = {imported},
timestamp = {2011-03-09T04:37:01.000+0100},
title = {Extracting Product Features and Opinions from
Reviews},
url = {http://www.cs.washington.edu/homes/etzioni/papers/emnlp05_opine.pdf},
year = 2005
}