{"title":"利用《变形金刚》的双向编码器表征识别印尼社交媒体上的厌女症(BERT)","authors":"Bagas Tri Wibowo, Dade Nurjanah, Hani Nurrahmi","doi":"10.1109/ICAIIC57133.2023.10067106","DOIUrl":null,"url":null,"abstract":"Misogyny is a behavior that hates or dislikes women Text classification can be used to identify misogyny text. One text classification method currently popular and proven to have good performance is the Bidirectional Encoder From Transformers (BERT). Fine-tuning is a method to transfer knowledge from a trained model to a new model to complete a new task. This study focuses on building a misogyny identification model with IndoBert pre-trained model provided by IndoNLU. The identification of Misogyny model obtained the best results with an accuracy value of 83.74% and by using K-fold cross-validation, the average validation value is 77.86%.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of Misogyny on Social Media in Indonesian Using Bidirectional Encoder Representations From Transformers (BERT)\",\"authors\":\"Bagas Tri Wibowo, Dade Nurjanah, Hani Nurrahmi\",\"doi\":\"10.1109/ICAIIC57133.2023.10067106\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Misogyny is a behavior that hates or dislikes women Text classification can be used to identify misogyny text. One text classification method currently popular and proven to have good performance is the Bidirectional Encoder From Transformers (BERT). Fine-tuning is a method to transfer knowledge from a trained model to a new model to complete a new task. This study focuses on building a misogyny identification model with IndoBert pre-trained model provided by IndoNLU. The identification of Misogyny model obtained the best results with an accuracy value of 83.74% and by using K-fold cross-validation, the average validation value is 77.86%.\",\"PeriodicalId\":105769,\"journal\":{\"name\":\"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAIIC57133.2023.10067106\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIIC57133.2023.10067106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of Misogyny on Social Media in Indonesian Using Bidirectional Encoder Representations From Transformers (BERT)
Misogyny is a behavior that hates or dislikes women Text classification can be used to identify misogyny text. One text classification method currently popular and proven to have good performance is the Bidirectional Encoder From Transformers (BERT). Fine-tuning is a method to transfer knowledge from a trained model to a new model to complete a new task. This study focuses on building a misogyny identification model with IndoBert pre-trained model provided by IndoNLU. The identification of Misogyny model obtained the best results with an accuracy value of 83.74% and by using K-fold cross-validation, the average validation value is 77.86%.