{"title":"印地语-英语代码混合数据中的姿态检测","authors":"Jethva Utsav, Dhaiwat Kabaria, Ribhu Vajpeyi, Mohit Mina, Vivek Srivastava","doi":"10.1145/3371158.3371226","DOIUrl":null,"url":null,"abstract":"Social media sites such as Twitter, Facebook, and many other microblogging forums have emerged as a platform for people to express their opinions and perspectives on different events. People often tend to take a stance; in favor, against or neutral towards a particular topic on these platforms. Hindi and English are the most widely used languages on social media platforms in India, and the user predominantly expresses their opinions in Hindi-English code-mixed texts. As a result, knowing the diverse opinions of the masses is difficult. We target to classify Hindi-English code-mixed tweets based on their stance. A dataset consisting of 3545 English-Hindi code-mixed tweets with Demonetisation in the target is used in the experiments so far. We present a new stance annotated dataset of English-Hindi 4219 code-mixed tweets with the abrogation of article 370 in focus.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Stance Detection in Hindi-English Code-Mixed Data\",\"authors\":\"Jethva Utsav, Dhaiwat Kabaria, Ribhu Vajpeyi, Mohit Mina, Vivek Srivastava\",\"doi\":\"10.1145/3371158.3371226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Social media sites such as Twitter, Facebook, and many other microblogging forums have emerged as a platform for people to express their opinions and perspectives on different events. People often tend to take a stance; in favor, against or neutral towards a particular topic on these platforms. Hindi and English are the most widely used languages on social media platforms in India, and the user predominantly expresses their opinions in Hindi-English code-mixed texts. As a result, knowing the diverse opinions of the masses is difficult. We target to classify Hindi-English code-mixed tweets based on their stance. A dataset consisting of 3545 English-Hindi code-mixed tweets with Demonetisation in the target is used in the experiments so far. We present a new stance annotated dataset of English-Hindi 4219 code-mixed tweets with the abrogation of article 370 in focus.\",\"PeriodicalId\":360747,\"journal\":{\"name\":\"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3371158.3371226\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3371158.3371226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Social media sites such as Twitter, Facebook, and many other microblogging forums have emerged as a platform for people to express their opinions and perspectives on different events. People often tend to take a stance; in favor, against or neutral towards a particular topic on these platforms. Hindi and English are the most widely used languages on social media platforms in India, and the user predominantly expresses their opinions in Hindi-English code-mixed texts. As a result, knowing the diverse opinions of the masses is difficult. We target to classify Hindi-English code-mixed tweets based on their stance. A dataset consisting of 3545 English-Hindi code-mixed tweets with Demonetisation in the target is used in the experiments so far. We present a new stance annotated dataset of English-Hindi 4219 code-mixed tweets with the abrogation of article 370 in focus.
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