Abstract: Abstract: The multi-label sentiment classification task aims to deal with the problem that an instance may be associated with multiple sentiment labels. Most existing multi-label sentiment classification models were designed based on complete data，and their performance and sentiment were easily affected by the incompleteness of data itself. To address this problem，a cross-language multi-label sentiment classification model based on stacked denoising autoencoder is proposed， and a loss function is introduced to compensate for the loss caused by training. In this model， the word vectors are denoised by the stacked denoising autoencoder to construct the low-dimensional features of the original data. This reduces the noise interference in feature space and provides effective feature representation for downstream tasks. In the multi-label sentiment classification experiment of SemEval2018 three language datasets （English， Arabic and Spanish）， the micro_F1 score， macro_F1 score and jaccard indexes of the model on the test set are all improved. Macro_F1 is improved by about 0.82， 1.45 and 1.83 percentage points， respectively.

Key words: Key words: multi-label classification, sentiment classification, incomplete data, BERT, stacked denoising autoencoder（SDAE）