{"title":"基于BERT、LSTM和认知词典的情感分析","authors":"Hsiao-Ting Tseng, Y. Zheng, Chen-Chiung Hsieh","doi":"10.1109/ICCE-Taiwan55306.2022.9868974","DOIUrl":null,"url":null,"abstract":"Due to the epidemic situation, in order to greatly reduce the infection risk of face-to-face interviews, this paper implements the BERT combined with RCNN to judge the positive and negative directions of the text, and then uses BERT's next sentence prediction (NSP) to find out the topic-related sentences in the text. Finally, a cognitive dictionary is used to calculate the degree of agreement or disagreement, so as to obtain the degree of support of the reviewer. This paper is also useful for letting visitors or authors know what the respondents' views are.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Sentiment Analysis using BERT, LSTM, and Cognitive Dictionary\",\"authors\":\"Hsiao-Ting Tseng, Y. Zheng, Chen-Chiung Hsieh\",\"doi\":\"10.1109/ICCE-Taiwan55306.2022.9868974\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the epidemic situation, in order to greatly reduce the infection risk of face-to-face interviews, this paper implements the BERT combined with RCNN to judge the positive and negative directions of the text, and then uses BERT's next sentence prediction (NSP) to find out the topic-related sentences in the text. Finally, a cognitive dictionary is used to calculate the degree of agreement or disagreement, so as to obtain the degree of support of the reviewer. This paper is also useful for letting visitors or authors know what the respondents' views are.\",\"PeriodicalId\":164671,\"journal\":{\"name\":\"2022 IEEE International Conference on Consumer Electronics - Taiwan\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Consumer Electronics - Taiwan\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9868974\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Consumer Electronics - Taiwan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9868974","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sentiment Analysis using BERT, LSTM, and Cognitive Dictionary
Due to the epidemic situation, in order to greatly reduce the infection risk of face-to-face interviews, this paper implements the BERT combined with RCNN to judge the positive and negative directions of the text, and then uses BERT's next sentence prediction (NSP) to find out the topic-related sentences in the text. Finally, a cognitive dictionary is used to calculate the degree of agreement or disagreement, so as to obtain the degree of support of the reviewer. This paper is also useful for letting visitors or authors know what the respondents' views are.