The User Review Domain

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).

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.

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.