Abstract: Sentiment classification has important application value for downstream applications,including recommendation system,automatic question answering and reading comprehension.It is an important research direction in the field of natural language processing.The task of sentiment classification depends on global and local information hidden in context.However,exis-ting neural network models can not capture the local and global information of context at the same time.In this paper,a recurrent convolutional attention model (LSTM-CNN-ATT,LCA) is proposed for single label and multi-label sentiment classification tasks.It uses attention mechanism to fuse the local information extraction ability of convolutional neural network and the global information extraction ability of recurrent neural network,including word embedding layer,context representation layer,convolution layer and attention layer.For the multi-label sentiment classification task,the topic information is added to the attention layer to further guide the accurate extraction of multi-label emotion tendency.The F1 index on two single label datasets reaches 82.1%,which is equivalent to the frontier single label model.On two multi-label datasets,the experimental results on small datasets are close to the benchmark model,and the F1 index on large datasets reaches 78.38%,which is higher than the state-of-the-art model.It indicates that LCA model has high stability and strong universality.

Key words: Attention mechanism, Convolutional neural network, Recurrent neural network, Sentiment classification