Difference between revisions of "Opinion mining"
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* Document level | * Document level | ||
| − | ** [[relatedPaper::Turney,2002]] presented an approach to calculate the opinion orientation using the Web as a corpus. The input review is classified based on the average semantic orientation of the phrases in the review. They have used PMI-IR technique to measure the semantic orientation of each phrase in the review. | + | ** [[relatedPaper::Turney,2002]] presented an approach to calculate the opinion orientation using the Web as a corpus. The input review is classified based on the average semantic orientation of all the phrases in the review. They have used PMI-IR technique to measure the semantic orientation of each phrase in the review. |
** [[Turney and Littman, 2003]] expanded [[Turney,2002]] work using cosine distance in latent semantic analysis as the distance measure. | ** [[Turney and Littman, 2003]] expanded [[Turney,2002]] work using cosine distance in latent semantic analysis as the distance measure. | ||
| − | ** [[Dave et al.,2003]] introduced a novel approach to classify reviews in Amazon.com using normalized term frequency | + | ** [[Dave et al.,2003]] introduced a novel approach to classify reviews in Amazon.com using normalized term frequency in uni-gram, bi-gram and tri-gram. |
Latest revision as of 11:10, 2 December 2010
Summary
Opinion mining is a problem in the field of information extraction that aims to automatically extract opinion expressions from product reviews. Also one of the goal of the opinion mining techniques is to determine the opinion direction of a review.
Common Approaches
Generally there are two approaches for opinion mining: 1- document level and 2- feature level opinion mining.
- Document level
- Turney,2002 presented an approach to calculate the opinion orientation using the Web as a corpus. The input review is classified based on the average semantic orientation of all the phrases in the review. They have used PMI-IR technique to measure the semantic orientation of each phrase in the review.
- Turney and Littman, 2003 expanded Turney,2002 work using cosine distance in latent semantic analysis as the distance measure.
- Dave et al.,2003 introduced a novel approach to classify reviews in Amazon.com using normalized term frequency in uni-gram, bi-gram and tri-gram.
- Feature level
- Zhuang et al., 2006 introduced a novel technique to classify movie reviews by extracting high frequency feature keywords.
- Liu, 2004 uses a statistical rule-based approach to extract high frequency feature words.
