{"id":148334,"date":"2002-01-01T00:00:00","date_gmt":"2002-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/thumbs-up-or-thumbs-down-semantic-orientation-applied-to-unsupervised-classification-of-reviews\/"},"modified":"2018-10-16T21:05:20","modified_gmt":"2018-10-17T04:05:20","slug":"thumbs-up-or-thumbs-down-semantic-orientation-applied-to-unsupervised-classification-of-reviews","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/thumbs-up-or-thumbs-down-semantic-orientation-applied-to-unsupervised-classification-of-reviews\/","title":{"rendered":"Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., &#8220;subtle nuances&#8221;) and a negative semantic orientation when it has bad associations (e.g., &#8220;very cavalier&#8221;). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word &#8220;excellent&#8221; minus the mutual information between the given phrase and the word &#8220;poor&#8221;. A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations [&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-02, 40th Annual 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":"417\u2013424","msr_page_range_start":"417","msr_page_range_end":"424","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of ACL-02, 40th Annual Meeting of the Association for Computational 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