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In the field of car evaluation, more and more netizens choose to expresstheir opinions on the Internet platform, and these comments will affect thedecision-making of buyers and the trend of car word-of-mouth. As an importantbranch of natural language processing (NLP), sentiment analysis provides aneffective research method for analyzing the sentiment types of massive carreview texts. However, due to the lexical professionalism and large text noiseof review texts in the automotive field, when a general sentiment analysismodel is applied to car reviews, the accuracy of the model will be poor. Toovercome these above challenges, we aim at the sentiment analysis task of carreview texts. From the perspective of word vectors, pre-training is carried outby means of whole word mask of proprietary vocabulary in the automotive field,and then training data is carried out through the strategy of an adversarialtraining set. Based on this, we propose a car review text sentiment analysismodel based on adversarial training and whole word mask BERT(ATWWM-BERT).

A sentiment analysis model for car review texts based on adversarial training and whole word mask BERT