Aiming at the problem that most of existing LSTM-based methods for sentence sentiment classification don’t take into account the part-of-speech (POS) information of words，a neural network model，PALSTM， which combines POS and self-attention mechanism，was proposed and applied to sentence sentiment classification． Firstly，PALSTM used pre-trained word vectors and POS tagging tool to give the semantic and POS word vector rep- resentations of words in the sentences，and then took them as the inputs of a LSTM so as to capture the long-term dependence of words on content and part-of-speech，which effectively compensates for the common LSTM networks relying solely on the co-occurrence information of words in pre-trained word vectors． Secondly，the self-attention mechanism was used to learn the position information about words in the sentences and build the corresponding po- sition weight matrix，which yields the final semantic representations of sentences． Finally，the results was classi- fied and outputted via a multi-layer perceptron． The experiments show that PALSTM outperforms common LSTM and attentional LSTM models on some open corpus，i. e． Movie Reviews，Internet Movie Database，Stanford Sen- timent Treebank binary and fine-grained classification．