Abstract: Traditional cross-language sentiment classification methods based on machine translation are affected by the performance of machine translation, resulting in lower accuracy of sentiment classification in low-resource languages such as Vietnamese. Aiming at the problem of imbalance between source language and target language markup resources, this paper proposes a cross-language sentiment classification  model based on sentiment semantic confrontation. Firstly, the sentences and the emotional words in the sentences are spliced, and the spliced sentences are jointly represented by the convolutional neural network, and the emotional semantic representations in the monolingual semantic space are obtained respectively. Secondly, through the confrontation network, the emotional semantic representations of labeled data and unlabeled data are aligned in the bilingual emotional semantic space. Finally, the most significant representations of sentences and emotional words are spliced together to obtain the results of emotional orientation classification. The experimental results based on the Chinese-English public data set and the Chinese-Vietnamese data set we constructed show that, compared with the mainstream methods of cross-language sentiment classification, the proposed method achieves bilingual sentimental semantic alignment, and can effectively improve the accuracy of sentimental orientation analysis of Vietnamese. The proposed method has obvious advantages in different language pairs.

Key words: emotional semantic representation, bilingual word embedding, low-resource language；cross-language sentiment classification