Text Sentiment Classification Based on LSTM-TCN Hybrid Model and Attention Mechanism.

Aiming at the problem that traditional convolutional neural networks cannot fully capture text features during feature extraction, and a single model cannot effectively extract deep text features, this paper proposes a text sentiment classification method based on the attention mechanism of LSTM-TCN hybrid model. First, use the Word2vec word vector model to transform words into the form of word vectors. Secondly, with Long Short-Term Memory (LSTM) to obtain the serialized feature information of the text, and then combined with the Temporal Convolutional Network (TCN) to sample and calculate the text features to extract more comprehensive feature information of the text. Then, combined with the attention mechanism, the influence of important information on the sentiment of the text is considered, and the effect of text sentiment classification is optimized. Finally, use softmax function to classify sentiment in the text classification layer. Multiple sets of comparative experiments were conducted on two sets of Chinese data sets, and the accuracy of the text model reached 93.06% and 91.08%, respectively, proving that the proposed model performs better than the traditional single convolutional neural network.