Research on sentiment classification for netizens based on the BERT-BiLSTM-TextCNN model.

ABSTRACT: Sentiment analysis of netizens' comments can accurately grasp the psychology of netizens and reduce the risks brought by online public opinion. However, there is currently no effective method to solve the problems of short text, open word range, and sometimes reversed word order in comments. To better solve the above problems, this article proposes a hybrid model of sentiment classification, which is based on bidirectional encoder representations from transformers (BERT), bidirectional long short-term memory (BiLSTM) and a text convolution neural network (TextCNN) (BERT-BiLSTM-TextCNN). The experimental results show that (1) the hybrid model proposed in this article can better combine the advantages of BiLSTM and TextCNN; it not only captures local correlation while retaining context infor

SUBMITTER: Jiang X 

PROVIDER: S-EPMC9202631 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature