Picking the Fresh from the Rotten: Quote and Sentiment Extraction from Rotten Tomatoes Movie Reviews

Being able to quickly determine whether a movie is worth watching is one of the major appeals of the movie review website Rotten Tomatoes (www.rottentomatoes.com). The site gathers reviews from known movie critics, with each critic's review assigned a binary rating expressing the review’s sentiment, fresh (positive) or rotten (negative), and a quote, the single sentence/phrase from the review that best exemplifies that rating. This form capsule summarization allows users the ability to discern the consensus of a group of critics at a glance. A system was constructed to perform the "Rotten Tomatoes Task" of identifying the quote and rating from an unlabeled review. A maximum entropy classifier was trained on a corpus of reviews from Rotten Tomatoes that had their ratings and accompanying quotes identified. Lexical and positional features were used. In addition, the notion of regional coherence, from text-segmentation work, was applied to create features that would help identify potential quotes from a review. A discussion of the nature of this "Rotten Tomatoes Task" is given, as well as future avenues of investigation, including alternative evaluation methods.