基于CNN和B-LSTM的文本处理模型研究
Research on text processing model based on CNN and B-LSTM
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摘要: 针对文本情感分类准确率不高的问题，在卷积神经网络CNN和栈式双向长短时记忆网络B-LSTM的基础上，提出了一种新的情感分析训练模型CNN-B-LSTM.该模型利用CNN的卷积操作对词向量进行处理，提取词向量的强度特征，再输入到B-LSTM中进行上层建模，对句子进行处理.结果表明：CNN-B-LSTM模型的情感分类准确率比CNN和B-LSTM模型更高，差错率大约分别降低了4%和1%，具有一定的效果优势.
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关键词:
- 文本情感分类 /
- 卷积神经网络 /
- 长短时记忆网络 /
- PaddlePaddle
Abstract: Aiming at the problem of low accuracy of text sentiment classification, CNN-B-LSTM, a new sentiment analysis training model based on CNN and B-LSTM was presented. The convolution operation processed the word vector to extract the intensity characteristics of the word vector, and then inputed it into the B-LSTM to perform the upper level modeling and used it to process the sentences.The results showed that the proposed CNN-B-LSTM model had higher sentiment classification accuracy,the error rates decreased by 4% and 1%,respectively.It was superior to B-LSTM and CNN models in sentiment classification.-
Key words:
- text sentiment classification /
- CNN /
- LSTM /
- PaddlePaddle
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