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.
Data: http://www.cs.cornell.edu/people/pabo/movie-review-data/
Relevant talk slides: What is the matter? Explorations in document classification (UAI 2004, pdf)
BibTeX entry:
@InProceedings{Pang+Lee:05a,
author = {Bo Pang and Lillian Lee},
title = {Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales},
booktitle = {Proceedings of the ACL},
pages = {115--124},
year = 2005
}