{"title":"社交媒体帖子中的讽刺检测:情感分析的一个分支","authors":"Mansi Vats, Yashvardhan Soni","doi":"10.1201/9780429444272-56","DOIUrl":null,"url":null,"abstract":"This paper is written before doing any practical work on the mentioned topic. The paper is based on the re-search done to find methods to detect sarcasm in the social media posts. The language that will be used for this is the R language. We have given a brief introduction of Sentiment Analysis, which sarcasm detection is a part of. We have defined and described Natural Language Processing (NLP), through which the machine can understand the human language to detect sarcasm. We have also described the steps for sarcasm detection and a comparison between different machine learning algorithms to choose for sarcasm detection. In the end, the result will be evaluated that whether the machine is able to detect sarcasm with atleast 80% accuracy or not.","PeriodicalId":355169,"journal":{"name":"Communication and Computing Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sarcasm detection in social media posts: A division of sentiment analysis\",\"authors\":\"Mansi Vats, Yashvardhan Soni\",\"doi\":\"10.1201/9780429444272-56\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper is written before doing any practical work on the mentioned topic. The paper is based on the re-search done to find methods to detect sarcasm in the social media posts. The language that will be used for this is the R language. We have given a brief introduction of Sentiment Analysis, which sarcasm detection is a part of. We have defined and described Natural Language Processing (NLP), through which the machine can understand the human language to detect sarcasm. We have also described the steps for sarcasm detection and a comparison between different machine learning algorithms to choose for sarcasm detection. In the end, the result will be evaluated that whether the machine is able to detect sarcasm with atleast 80% accuracy or not.\",\"PeriodicalId\":355169,\"journal\":{\"name\":\"Communication and Computing Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communication and Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1201/9780429444272-56\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communication and Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1201/9780429444272-56","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sarcasm detection in social media posts: A division of sentiment analysis
This paper is written before doing any practical work on the mentioned topic. The paper is based on the re-search done to find methods to detect sarcasm in the social media posts. The language that will be used for this is the R language. We have given a brief introduction of Sentiment Analysis, which sarcasm detection is a part of. We have defined and described Natural Language Processing (NLP), through which the machine can understand the human language to detect sarcasm. We have also described the steps for sarcasm detection and a comparison between different machine learning algorithms to choose for sarcasm detection. In the end, the result will be evaluated that whether the machine is able to detect sarcasm with atleast 80% accuracy or not.