{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/seeing-stars-exploiting-class-relationships","title":"Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales","arxiv_id":"cs/0506075","date":"2005-06-17","proceeding":null,"authors":["Bo Pang","Lillian Lee"],"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.","url_abs":"https://arxiv.org/abs/cs/0506075v1","url_pdf":"https://arxiv.org/pdf/cs/0506075v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"seeing-stars-exploiting-class-relationships","repo_url":"https://github.com/hc495/staicc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-categorization","task_name":"Text Categorization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=cs%2F0506075","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}