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