Seeing stars

Bo Pang, Lillian Lee
2005 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05  
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 "four
more » ... s" than to "one star". We first evaluate human performance at the task. Then, we apply a metaalgorithm, based on a metric labeling formulation of the problem, that alters a given ¦ -ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem.
doi:10.3115/1219840.1219855 dblp:conf/acl/PangL05 fatcat:4kzdcfsknfaive7hyl6mf4wtg4