.png){kind=icon}
返回
Text sentiment analysis based on BERT-CBLBGA
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2023.109019.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Current word vector techniques can generate effective word representations from massive text data, and different deep learning models can extract various textual semantic and grammatical features from different perspectives and levels. To extract richer text sentiment features, we propose a fused model of text sentiment classification called BERT-CBLBGA, which combines BERT with multiple feature extraction deep learning models. First, BERT is utilized to generate word embedding of the text, and the obtained feature representations are then fed into TextCNN, BiLSTM, and BiGRU to extract local and global sentiment features of the text. Then we use self-attention to fuse the output of TextCNN, BiLSTM, and BiGRU to enhance the ability to extract text sentiment classification features. Finally, a classifier is employed to classify the extracted features. Experimental results on four different domain text datasets demonstrate that the BERT-CBLBGA outperforms the baseline models in terms of extracting text sentiment features and achieves the best classification performance.
Keyword:
Feature extraction
Feature fusion
Sentiment analysis
BERT-CBLBGA
Deep learning
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
暂无机构信息
引用论文
GENETIC DRIFT AND FOUNDER EFFECT IN NATIVE VERSUS INTRODUCED POPULATIONS OF AN INVADING PLANT,LYTHRUM SALICARIA(LYTHRACEAE).png)
Evolution
OAAI
EvolutionIF0
OAAI分享收藏分享收藏分享收藏
查看更多内容