Fangju Ran, Chen Xiong, Meng-yao Lu, Tianqing Yang
{"title":"基于BERT和Bi-LSTM的高校网络舆情分析研究","authors":"Fangju Ran, Chen Xiong, Meng-yao Lu, Tianqing Yang","doi":"10.1117/12.2671058","DOIUrl":null,"url":null,"abstract":"This paper proposes a method of emotion analysis based on BERT BiLSTM. Firstly, BERT is used to realize the word vectorization, and then Bilstm is constructed to extract semantic features for emotional analysis. In the experiment, the model designed in this paper is compared with the emotional dictionary, SVM, Word2vec LSTM, BERT TextCNN on the college online public opinion comment dataset, and the experiment proves that the accuracy of this model has been improved.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on university network public opinion sentiment analysis based on BERT and Bi-LSTM\",\"authors\":\"Fangju Ran, Chen Xiong, Meng-yao Lu, Tianqing Yang\",\"doi\":\"10.1117/12.2671058\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a method of emotion analysis based on BERT BiLSTM. Firstly, BERT is used to realize the word vectorization, and then Bilstm is constructed to extract semantic features for emotional analysis. In the experiment, the model designed in this paper is compared with the emotional dictionary, SVM, Word2vec LSTM, BERT TextCNN on the college online public opinion comment dataset, and the experiment proves that the accuracy of this model has been improved.\",\"PeriodicalId\":227528,\"journal\":{\"name\":\"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)\",\"volume\":\"199 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2671058\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2671058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on university network public opinion sentiment analysis based on BERT and Bi-LSTM
This paper proposes a method of emotion analysis based on BERT BiLSTM. Firstly, BERT is used to realize the word vectorization, and then Bilstm is constructed to extract semantic features for emotional analysis. In the experiment, the model designed in this paper is compared with the emotional dictionary, SVM, Word2vec LSTM, BERT TextCNN on the college online public opinion comment dataset, and the experiment proves that the accuracy of this model has been improved.
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