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

Simple item page
dc.creatorPang, Bo
dc.creatorLee, Lillian
dc.date2005-06-17
dc.date.accessioned2026-07-07T03:23:09Z
dc.date.available2026-07-07T03:23:09Z
dc.descriptionWe 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 meta-algorithm, 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.
dc.descriptionTo appear, Proceedings of ACL 2005
dc.identifierhttps://arxiv.org/abs/cs/0506075
dc.identifierhttp://arxiv.org/abs/cs/0506075
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32833
dc.subjectComputation and Language
dc.subjectMachine Learning
dc.subjectI.2.7; I.2.6
dc.titleSeeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
dc.typetext

Files

Collections

arXiv