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We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five "stars"). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, "three stars" is intuitively closer to "fourdoi:10.3115/1219840.1219855 dblp:conf/acl/PangL05 fatcat:4kzdcfsknfaive7hyl6mf4wtg4