{"title":"预测电视情景喜剧对话中的幽默反应","authors":"D. Bertero, Pascale Fung","doi":"10.1109/ICASSP.2016.7472785","DOIUrl":null,"url":null,"abstract":"We propose a method to predict humor response in dialog using acoustic and language features. We use data from two popular TV sitcoms - \"The Big Bang Theory\" and \"Seinfeld\" - to predict how the audience responds to humor. Due to the sequentiality of humor response in dialogues we use a Conditional Random Field as classifier/predictor. Our method is relatively effective, with a maximum precision obtained of 72.1% in \"Big Bang\" and 60.2% in \"Seinfeld\". Experiments show that audio, speed, word and sentence length features are the most effective. This work is applicable to develop appropriate machine response empathetic to emotion in dialog, in addition to humor.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1962 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Predicting humor response in dialogues from TV sitcoms\",\"authors\":\"D. Bertero, Pascale Fung\",\"doi\":\"10.1109/ICASSP.2016.7472785\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a method to predict humor response in dialog using acoustic and language features. We use data from two popular TV sitcoms - \\\"The Big Bang Theory\\\" and \\\"Seinfeld\\\" - to predict how the audience responds to humor. Due to the sequentiality of humor response in dialogues we use a Conditional Random Field as classifier/predictor. Our method is relatively effective, with a maximum precision obtained of 72.1% in \\\"Big Bang\\\" and 60.2% in \\\"Seinfeld\\\". Experiments show that audio, speed, word and sentence length features are the most effective. This work is applicable to develop appropriate machine response empathetic to emotion in dialog, in addition to humor.\",\"PeriodicalId\":165321,\"journal\":{\"name\":\"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\",\"volume\":\"1962 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.2016.7472785\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2016.7472785","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting humor response in dialogues from TV sitcoms
We propose a method to predict humor response in dialog using acoustic and language features. We use data from two popular TV sitcoms - "The Big Bang Theory" and "Seinfeld" - to predict how the audience responds to humor. Due to the sequentiality of humor response in dialogues we use a Conditional Random Field as classifier/predictor. Our method is relatively effective, with a maximum precision obtained of 72.1% in "Big Bang" and 60.2% in "Seinfeld". Experiments show that audio, speed, word and sentence length features are the most effective. This work is applicable to develop appropriate machine response empathetic to emotion in dialog, in addition to humor.
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