%0 Journal Article %1 pang2005seeing %A Pang, Bo %A Lee, Lillian %D 2005 %K inter-rater-agreement sentiment-analysis %N 1 %T Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales %U http://arxiv.org/abs/cs/0506075 %V 3 %X 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 öne 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.

@article{pang2005seeing, 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.}, added-at = {2019-05-18T14:23:05.000+0200}, archiveprefix = {arXiv}, arxivid = {cs/0506075}, author = {Pang, Bo and Lee, Lillian}, biburl = {https://www.bibsonomy.org/bibtex/2fee22fabda83897d978da95b3064531e/ghagerer}, eprint = {0506075}, file = {:home/ghagerer/Downloads/0506075.pdf:pdf}, interhash = {60737e48851552e1b148d227e1d9dc85}, intrahash = {fee22fabda83897d978da95b3064531e}, keywords = {inter-rater-agreement sentiment-analysis}, number = 1, primaryclass = {cs}, timestamp = {2019-05-18T16:10:54.000+0200}, title = {{Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales}}, url = {http://arxiv.org/abs/cs/0506075}, volume = 3, year = 2005 }