{"id":148310,"date":"2005-01-01T00:00:00","date_gmt":"2005-01-01T00:00:00","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/msr-research-item\/seeing-stars-exploiting-class-relationships-for-sentiment-categorization-with-respect-to-rating-scales\/"},"modified":"2018-10-16T21:01:42","modified_gmt":"2018-10-17T04:01:42","slug":"seeing-stars-exploiting-class-relationships-for-sentiment-categorization-with-respect-to-rating-scales","status":"publish","type":"msr-research-item","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/seeing-stars-exploiting-class-relationships-for-sentiment-categorization-with-respect-to-rating-scales\/","title":{"rendered":"Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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&#8217;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 metaalgorithm, based on a metric labeling formulation of the problem, that alters a given <em>n<\/em>-ary classifier&#8217;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.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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&#8217;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 [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Association for Computational Linguistics","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of ACL-05, 43nd Meeting of the Association for Computational Linguistics","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"115\u2013124","msr_page_range_start":"115","msr_page_range_end":"124","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of ACL-05, 43nd Meeting of the Association for Computational 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