Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales

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 stars 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 n-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.