{"title":"遗书情绪分类的统计与相似方法。","authors":"Kirk Roberts, Sanda M Harabagiu","doi":"10.4137/BII.S8958","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper we report on the approaches that we developed for the 2011 i2b2 Shared Task on Sentiment Analysis of Suicide Notes. We have cast the problem of detecting emotions in suicide notes as a supervised multi-label classification problem. Our classifiers use a variety of features based on (a) lexical indicators, (b) topic scores, and (c) similarity measures. Our best submission has a precision of 0.551, a recall of 0.485, and a F-measure of 0.516.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"195-204"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S8958","citationCount":"10","resultStr":"{\"title\":\"Statistical and similarity methods for classifying emotion in suicide notes.\",\"authors\":\"Kirk Roberts, Sanda M Harabagiu\",\"doi\":\"10.4137/BII.S8958\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this paper we report on the approaches that we developed for the 2011 i2b2 Shared Task on Sentiment Analysis of Suicide Notes. We have cast the problem of detecting emotions in suicide notes as a supervised multi-label classification problem. Our classifiers use a variety of features based on (a) lexical indicators, (b) topic scores, and (c) similarity measures. Our best submission has a precision of 0.551, a recall of 0.485, and a F-measure of 0.516.</p>\",\"PeriodicalId\":88397,\"journal\":{\"name\":\"Biomedical informatics insights\",\"volume\":\"5 Suppl. 1\",\"pages\":\"195-204\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.4137/BII.S8958\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical informatics insights\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4137/BII.S8958\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2012/1/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical informatics insights","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4137/BII.S8958","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2012/1/30 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Statistical and similarity methods for classifying emotion in suicide notes.
In this paper we report on the approaches that we developed for the 2011 i2b2 Shared Task on Sentiment Analysis of Suicide Notes. We have cast the problem of detecting emotions in suicide notes as a supervised multi-label classification problem. Our classifiers use a variety of features based on (a) lexical indicators, (b) topic scores, and (c) similarity measures. Our best submission has a precision of 0.551, a recall of 0.485, and a F-measure of 0.516.
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