Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
P. Turney. ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, page 417--424. Morristown, NJ, USA, Association for Computational Linguistics, (2001)
DOI: http://dx.doi.org/10.3115/1073083.1073153
Abstract
This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
%0 Conference Paper
%1 Turney02
%A Turney, Peter D.
%B ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
%C Morristown, NJ, USA
%D 2001
%I Association for Computational Linguistics
%K POS SentimentAnalysis TextCategorization semantic unsupervised
%P 417--424
%R http://dx.doi.org/10.3115/1073083.1073153
%T Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
%U http://portal.acm.org/citation.cfm?id=1073153
%X This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
@inproceedings{Turney02,
abstract = {This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.},
added-at = {2008-05-27T18:52:09.000+0200},
address = {Morristown, NJ, USA},
author = {Turney, Peter D.},
biburl = {https://www.bibsonomy.org/bibtex/220239379416a775558215354139efcaf/mkroell},
booktitle = {ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics},
description = {Thumbs up or thumbs down?},
doi = {http://dx.doi.org/10.3115/1073083.1073153},
interhash = {a2b7fbbe0f449d1ed070ff31ef4cad79},
intrahash = {20239379416a775558215354139efcaf},
keywords = {POS SentimentAnalysis TextCategorization semantic unsupervised},
location = {Philadelphia, Pennsylvania},
pages = {417--424},
publisher = {Association for Computational Linguistics},
timestamp = {2009-08-06T14:49:39.000+0200},
title = {Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews},
url = {http://portal.acm.org/citation.cfm?id=1073153},
year = 2001
}