Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
<|reference_start|>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 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.<|reference_end|>
@article{pang2005seeing,
    title={Seeing stars: Exploiting class relationships for sentiment
  categorization with respect to rating scales},
    author={Bo Pang and Lillian Lee},
    journal={arXiv preprint arXiv:cs/0506075},
    year={2005},
    archivePrefix={arXiv},
    eprint={cs/0506075},
    primaryClass={cs.CL cs.LG}
}