{"title":"利用双向长短期记忆检测推特中对印度的负面情绪的情感分析实现","authors":"Muhammad Kemal Hernandi, S. Wibowo, S. Suyanto","doi":"10.1109/IAICT52856.2021.9532546","DOIUrl":null,"url":null,"abstract":"Sentiment analysis is the method of extracting opinions from texts written in human language. Sentiment analysis can be used to analyze and evaluate the customer experience of the services that have been provided. With easy access to social media, sentiment analysis can be applied from people's comments on social media. One of the social media that is suitable for sentiment analysis is Twitter. In this paper, we focus on negative sentiment detection using tweets on Twitter by Indihome consumers. The system is designed to apply sentiment analysis using the BiLSTM method. Using BiLSTM, the accuracy 88 % is achieved.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Sentiment Analysis Implementation For Detecting Negative Sentiment Towards Indihome In Twitter Using Bidirectional Long Short Term Memory\",\"authors\":\"Muhammad Kemal Hernandi, S. Wibowo, S. Suyanto\",\"doi\":\"10.1109/IAICT52856.2021.9532546\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sentiment analysis is the method of extracting opinions from texts written in human language. Sentiment analysis can be used to analyze and evaluate the customer experience of the services that have been provided. With easy access to social media, sentiment analysis can be applied from people's comments on social media. One of the social media that is suitable for sentiment analysis is Twitter. In this paper, we focus on negative sentiment detection using tweets on Twitter by Indihome consumers. The system is designed to apply sentiment analysis using the BiLSTM method. Using BiLSTM, the accuracy 88 % is achieved.\",\"PeriodicalId\":416542,\"journal\":{\"name\":\"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAICT52856.2021.9532546\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAICT52856.2021.9532546","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sentiment Analysis Implementation For Detecting Negative Sentiment Towards Indihome In Twitter Using Bidirectional Long Short Term Memory
Sentiment analysis is the method of extracting opinions from texts written in human language. Sentiment analysis can be used to analyze and evaluate the customer experience of the services that have been provided. With easy access to social media, sentiment analysis can be applied from people's comments on social media. One of the social media that is suitable for sentiment analysis is Twitter. In this paper, we focus on negative sentiment detection using tweets on Twitter by Indihome consumers. The system is designed to apply sentiment analysis using the BiLSTM method. Using BiLSTM, the accuracy 88 % is achieved.