tailieunhanh - Báo cáo khoa học: "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 (., 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 metaalgorithm, based on a metric labeling formulation of the. | Seeing stars Exploiting class relationships for sentiment categorization with respect to rating scales Bo Pang1 3 and Lillian Lee1 2 3 1 Department of Computer Science Cornell University 2 Language Technologies Institute Carnegie Mellon University 3 Computer Science Department Carnegie Mellon University 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 . 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 metaalgorithm 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 signif-cant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem. 1 Introduction There has recently been a dramatic surge of interest in sentiment analysis as more and more people become aware of the scientific challenges posed and the scope of new applications enabled by the processing of subjective language. The papers collected by Qu Shanahan and Wiebe 2004 form a representative sample of research in the area. Most prior work on the specifc problem of categorizing expressly opinionated text has focused on the binary distinction of positive vs. negative Turney 2002 Pang Lee and Vaithyanathan 2002 Dave Lawrence and Pennock 2003 Yu and Hatzivas-siloglou 2003 . But it is often helpful to have more information than this binary distinction provides especially if one is ranking items by .