{"title":"YouTube视频中具有英语非正式言语语言特征的识别","authors":"Christopher R. Cooper","doi":"10.1016/j.acorp.2023.100068","DOIUrl":null,"url":null,"abstract":"<div><p>YouTube is becoming an increasingly popular entertainment platform, with videos catering to a wide range of interests. If L2 users are to become proficient in the primary form of language, conversation, then the affordances created by YouTube videos containing informal speech could be very useful. In the current study a near-random corpus of 2602 YouTube video transcripts was compiled and 200 randomly selected texts from the Spoken BNC2014 (Love et al., 2017) were used as a reference corpus representing informal spoken English. The texts were tagged with 67 linguistic features as part of an additive multi-dimensional analysis. The dimension scores for each text were used in a cluster analysis to investigate which texts clustered with the Spoken BNC2014 texts. A two-cluster solution was chosen with 666 YouTube texts and 171 Spoken BNC2014 texts in one cluster, and the remaining texts in the other cluster. A small sample of texts from each cluster was analysed in detail. It is shown that this method has the potential to identify videos featuring informal speech and that some videos with similar categories have a very different linguistic style.</p></div>","PeriodicalId":72254,"journal":{"name":"Applied Corpus Linguistics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The identification of YouTube videos that feature the linguistic features of English informal speech\",\"authors\":\"Christopher R. Cooper\",\"doi\":\"10.1016/j.acorp.2023.100068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>YouTube is becoming an increasingly popular entertainment platform, with videos catering to a wide range of interests. If L2 users are to become proficient in the primary form of language, conversation, then the affordances created by YouTube videos containing informal speech could be very useful. In the current study a near-random corpus of 2602 YouTube video transcripts was compiled and 200 randomly selected texts from the Spoken BNC2014 (Love et al., 2017) were used as a reference corpus representing informal spoken English. The texts were tagged with 67 linguistic features as part of an additive multi-dimensional analysis. The dimension scores for each text were used in a cluster analysis to investigate which texts clustered with the Spoken BNC2014 texts. A two-cluster solution was chosen with 666 YouTube texts and 171 Spoken BNC2014 texts in one cluster, and the remaining texts in the other cluster. A small sample of texts from each cluster was analysed in detail. It is shown that this method has the potential to identify videos featuring informal speech and that some videos with similar categories have a very different linguistic style.</p></div>\",\"PeriodicalId\":72254,\"journal\":{\"name\":\"Applied Corpus Linguistics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Corpus Linguistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S266679912300028X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Corpus Linguistics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266679912300028X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
YouTube正在成为一个越来越受欢迎的娱乐平台，其视频迎合了广泛的兴趣。如果第二语言使用者要精通语言的主要形式，即对话，那么YouTube视频中包含的非正式演讲可能非常有用。在当前的研究中，编译了2602个YouTube视频文本的近乎随机语料库，并从口语BNC2014 (Love et al.， 2017)中随机选择了200个文本作为代表非正式口语的参考语料库。作为附加的多维分析的一部分，这些文本被标记为67种语言特征。在聚类分析中使用每个文本的维度得分来调查哪些文本与口语BNC2014文本聚类。我们选择了一个双集群解决方案，其中666个YouTube文本和171个口语BNC2014文本在一个集群中，其余文本在另一个集群中。对每组文本的一小部分样本进行了详细分析。研究表明，这种方法有可能识别出具有非正式语言特征的视频，并且一些具有类似类别的视频具有非常不同的语言风格。
The identification of YouTube videos that feature the linguistic features of English informal speech
YouTube is becoming an increasingly popular entertainment platform, with videos catering to a wide range of interests. If L2 users are to become proficient in the primary form of language, conversation, then the affordances created by YouTube videos containing informal speech could be very useful. In the current study a near-random corpus of 2602 YouTube video transcripts was compiled and 200 randomly selected texts from the Spoken BNC2014 (Love et al., 2017) were used as a reference corpus representing informal spoken English. The texts were tagged with 67 linguistic features as part of an additive multi-dimensional analysis. The dimension scores for each text were used in a cluster analysis to investigate which texts clustered with the Spoken BNC2014 texts. A two-cluster solution was chosen with 666 YouTube texts and 171 Spoken BNC2014 texts in one cluster, and the remaining texts in the other cluster. A small sample of texts from each cluster was analysed in detail. It is shown that this method has the potential to identify videos featuring informal speech and that some videos with similar categories have a very different linguistic style.
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