{"id":148236,"date":"2004-01-01T00:00:00","date_gmt":"2004-01-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/approaches-for-automatically-tagging-affect\/"},"modified":"2018-10-16T20:48:20","modified_gmt":"2018-10-17T03:48:20","slug":"approaches-for-automatically-tagging-affect","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/approaches-for-automatically-tagging-affect\/","title":{"rendered":"Approaches for Automatically Tagging Affect"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The tagging of discourse is important not only for natural language processing research, but for many applications in the social sciences as well. This paper describes an evaluation of a range of different tagging techniques to automatically determine the attitude of speakers in transcribed psychiatric dialogues. It presents results in a marriage counseling domain that classifies the attitude and emotional commitment of the participants to a particular topic of discussion. It also gives results from the Switchboard Corpus to facilitate comparison for future work. Finally, it describes a new Java tool that learns attitude classifications using our techniques and provides a flexible, easy to use platform for tagging of texts.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The tagging of discourse is important not only for natural language processing research, but for many applications in the social sciences as well. This paper describes an evaluation of a range of different tagging techniques to automatically determine the attitude of speakers in transcribed psychiatric dialogues. 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