Abstract: Aiming at the problems of low accuracy, poor real-time performance and insufficient feature extraction in existing text sentiment analysis methods, a serial hybrid model based on Bi-directional Long Short-Term Memory neural network and Convolutional Neural Network (BiLSTM-CNN) was constructed. Firstly, the context information was extracted from the text by using Bi-directional Long Short Term Memory (BiLSTM) neural network. Then, the local semantic features were extracted from the context information by using Convolutional Neural Network (CNN). Finally, the emotional tendency of text was obtained by using Softmax. Compared with single models such as CNN, Long Short-Term Memory (LSTM) and BiLSTM, the proposed text sentiment analysis model increases the comprehensive evaluation index F1 by 2.02 percentage points, 1.18 percentage points and 0.85 percentage points respectively; and compared with the hybrid models such as LSTM and CNN (LSTM-CNN) and parallel features fusion of BiLSTM-CNN, the proposed text sentiment analysis model improves the comprehensive evaluation index F1 by 1.86 percentage points and 0.76 percentage points respectively. The experimental results show that the serial hybrid model based on BiLSTM-CNN has great value in practical applications.

Key words: text sentiment analysis, context information, semantic feature, Long Short-Term Memory (LSTM) neural network, Convolutional Neural Network (CNN)

摘要： 针对现有文本情感分析方法准确率不高、实时性不强以及特征提取不充分的问题，构建了双向长短时记忆神经网络和卷积神经网络（BiLSTM-CNN）的串行混合模型。首先，利用双向循环长短时记忆（BiLSTM）神经网络提取文本的上下文信息；然后，对已提取的上下文特征利用卷积神经网络（CNN）进行局部语义特征提取；最后，使用Softmax得出文本的情感倾向。通过与CNN、长短时记忆神经网络（LSTM）、BiLSTM等单一模型对比，所提出的文本情感分析模型在综合评价指标F1上分别提高了2.02个百分点、1.18个百分点和0.85个百分点；与长短时记忆神经网络和卷积神经网络（LSTM-CNN）、BiLSTM-CNN并行特征融合等混合模型对比，所提出的文本情感分析模型在综合评价指标F1上分别提高了1.86个百分点和0.76个百分点。实验结果表明，基于BiLSTM-CNN的串行混合模型在实际应用中具有较大的价值。

关键词: 文本情感分析, 上下文信息, 语义特征, 长短时记忆神经网络, 卷积神经网络