The invention relates to a multi-
feature fusion Chinese-overtopped news viewpoint
sentence extraction method, and belongs to the technical field of
natural language processing. Firstly, a cross-
language representation learning method is adopted to construct a Chinese-Vietnamese bilingual
word embedding model; and then calculating feature weights of the topic, emotion and position of the
sentence,and fusing the feature weight information into a coding layer and an attention mechanism to obtain representation of the
sentence in the aspects of topic, emotion, position and the like. And finally,viewpoint sentence classification is carried out according to the obtained sentence representation. Aiming at the problem that Chinese and Vietnamese marking resources are unbalanced, a Chinese-Vietnamese bilingual
word embedding model is constructed; according to the method, the sentences are extracted from the sentences, then the weights of the topics, the positions and the sentiment features ofthe sentences are calculated respectively, the sentence weights are fused into the word vectors and the attention mechanism respectively, sentence
semantic information and the sentiment, topic and position features are combined, and the accuracy of extracting the sentences of the Hami news
viewpoints can be effectively improved.