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

TL;DR

This paper tackles the challenge of rating inference in sentiment analysis by leveraging class relationships and a metric labeling approach to improve multi-point scale classification accuracy.

Contribution

It introduces a meta-algorithm that adjusts classifier outputs based on class similarities, enhancing rating scale prediction performance.

Findings

Meta-algorithm improves classification accuracy

Novel similarity measure tailored to rating scales

Significant gains over standard SVM approaches

Abstract

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 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.