Paper: Seeing Stars: Exploiting Class Relationships For Sentiment Categorization With Respect To Rating Scales

ACL ID P05-1015
Title Seeing Stars: Exploiting Class Relationships For Sentiment Categorization With Respect To Rating Scales
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

We address the rating-inference problem, wherein rather than simply decide whether a review is thumbs up or thumbs down, as in previous sentiment analy- sis work, one must determine an author’s evaluation with respect to a multi-point scale (e.g. , one to ve stars ). This task represents an interesting twist on stan- dard multi-class text categorization be- cause there are several different degrees of similarity between class labels; for ex- ample, three stars is intuitively closer to four stars than to one star. We rst evaluate human performance at the task. Then, we apply a meta- algorithm, based on a metric labeling for- mulation of the problem, that alters a given a6 -ary classi er’s output in an ex- plicit attempt to ensure that similar items receive similar labels. We show that t...