In the field of natural language processing,the Word2Vec representation method for static word vectors is unable to accurately capture the semantic information of sentences and text structures.Based on this problem,we propose a comment senti-ment analysis model based on ALBERT-CNN-BiLSTM-Attention,which not only effectively solves the problem of high number of parameters and low computational and storage efficiency of the traditional model,but also significantly improves the accuracy of sen-timent classification.The method firstly utilizes ALBERT model to obtain dynamic features of comment text,and introduces the at-tention mechanism module to obtain text keyword weights for the output results of BiLSTM,and then utilizes CNN to obtain local fea-tures of the text,and finally carries out sentiment classification of the comment content through Softmax layer.The experimental re-sults show that the model in this paper has a significant improvement in precision and recall compared with the traditional methods and other models of ALBERT,with the precision reaching 88.43%,the recall reaching 88.17%,and the F1 value reaching 88.30%.
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