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

TL;DR

This paper introduces ATWWM-BERT, a sentiment analysis model tailored for car reviews that leverages adversarial training and whole word masking to improve accuracy in the automotive domain.

Contribution

It presents a novel combination of adversarial training and whole word mask BERT specifically designed for car review sentiment analysis, addressing domain-specific challenges.

Findings

Enhanced sentiment classification accuracy on car review datasets

Effective handling of automotive-specific vocabulary and noise

Demonstrated superiority over general sentiment analysis models

Abstract

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).