Abstract: Text sentiment analysis is one of the classic fields of natural language processing.This paper proposes a text sentiment analysis model based on transformer feature extractor combined with multi-channel convolutional neural network.The model uses trsnsformer feature extractor to layer words and dynamically represent them on the basis of static word vectors trained by traditional Word2vector,Glove,etc.,and use Fine-Tuning for specific data sets for training,which effectively improves the representation of word vectors ability.The multi-channel convolutional neural network considers the dependence between word sequences in different size ranges,effectively extracts features and achieves the purpose of dimensionality reduction,can effectively capture the contextual semantic information of sentences,and enable the model to capture more semantic emotional information,improve the semantic expression ability of the text,and achieve the goal of emotional tendency classification through the Softmax activation function.The model is tested on the IMDb and SST-2 movie review datasets,and the accuracy rates on the test set reached 90.4% and 90.2%,indicating that the model proposed in this paper has better classification accuracy than the traditional word embedding combined with CNN or RNN.

Key words: Feature extractor, Multi-channel convolutional neural network, Sentiment classification, Transformer