论文标题
基于对抗性培训和整个词面具伯特的汽车评论文本的情感分析模型
A sentiment analysis model for car review texts based on adversarial training and whole word mask BERT
论文作者
论文摘要
在汽车评估领域，越来越多的网民选择在互联网平台上表达他们的意见，这些评论将影响买家的决策和汽车口碑的趋势。作为自然语言处理（NLP）的重要分支，情感分析提供了一种有效的研究方法，用于分析大规模汽车审查文本的情感类型。但是，由于汽车领域的词汇专业精神和综述文本的大量文本噪音，当将一般情感分析模型应用于汽车评论时，该模型的准确性将很差。为了克服上述挑战，我们旨在实现汽车审查文本的情感分析任务。从单词矢量的角度来看，预训练是通过在汽车领域中的整个专有词汇掩码进行的，然后通过对抗性训练集的策略进行训练数据。基于此，我们提出了一个基于对抗性训练和整个bask bert（atwwm-bert）的汽车评论文本情感分析模型。
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).
