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<H1><A NAME="SECTION00030000000000000000">
The User Review Domain</A>
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For our experiments, we worked with movie reviews. Our data source was Pang's released dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/) from their 2004 publication. The dataset contains 1000 positive reviews and 1000 negative reviews, each labeled with their true sentiment. The original data source was the Internet Movie Database (IMDb).
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Pang applied the bag-of-words method to positive and negative sentiment classification, but the same method can be extended to various other domains, including topic classification. We additionally chose to work with a set of 5000 Yelp reviews, 1000 for each of their five “star” rating. Yelp is a popular online urban city guide that houses reviews of restaurants, shopping areas, and businesses. Although a movie review and a Yelp review will differ in specialized vocabulary, audience, tone, etc., the ways that people convey sentiment (e.g. I loved it!) may not differ entirely. We wished to explore how training classifiers in one domain might generalize to neighbor domains.
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The domain of reviews is experimentally convenient because there are largely available on-line and because reviewers often summarize their overall sentiment with a machine-extractable rating indicator; hence, there was no need for hand-labeling of data.
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<ADDRESS>
Pranjal Vachaspati
2012-02-05
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