The sentiment classification aims to learn sentiment features from the annotated corpus and automatically predict the sentiment polarity of new sentiment text. However, people have different ways of expressing feelings in different domains. Thus, there are important differences in the characteristics of sentimental distribution across different domains. At the same time, in certain specific domains, due to the high cost of corpus collection, there is no annotated corpus available for the classification of sentiment. Therefore, it is necessary to leverage or reuse existing annotated corpus for training. In this article, we proposed a new algorithm for extracting central sentiment sentences in product reviews, and improved the pre-trained language model Bidirectional Encoder Representations from Transformers (BERT) to achieve the domain transfer for cross-domain sentiment classification. We used various pre-training language models to prove the effectiveness of the newly proposed joint algorithm for text-ranking and emotional words extraction, and utilised Amazon product reviews data set to demonstrate the effectiveness of our proposed domain-transfer framework. The experimental results of 12 different cross-domain pairs showed that the new cross-domain classification method was significantly better than several popular cross-domain sentiment classification methods.

情感分类旨在从带注释的语料库中学习情感特征，并自动预测新情感文本的情感极性。但是，人们在不同领域表达情感的方式不同。因此，跨不同领域的情感分布特征存在重要差异。同时，在某些特定领域中，由于语料库收集成本高昂，因此没有注释的语料库可用于情感分类。因此，有必要利用或重用现有的带注释的语料库进行训练。在本文中，我们提出了一种用于提取商品评论中的中心情感句子的新算法，并改进了预训练的语言模型（变压器）的双向编码器表示（BERT），以实现跨域情感分类的域传递。我们使用了各种预训练语言模型来证明新提出的联合算法对文本排名和情感词提取的有效性，并利用Amazon产品评论数据集来证明我们提出的域转移框架的有效性。12种不同的跨域对的实验结果表明，新的跨域分类方法明显优于几种流行的跨域情感分类方法。我们使用了各种预训练语言模型来证明新提出的联合算法对文本排名和情感词提取的有效性，并利用Amazon产品评论数据集来证明我们提出的域转移框架的有效性。12种不同的跨域对的实验结果表明，新的跨域分类方法明显优于几种流行的跨域情感分类方法。我们使用了各种预训练语言模型来证明新提出的联合算法对文本排名和情感词提取的有效性，并利用Amazon产品评论数据集来证明我们提出的域转移框架的有效性。12种不同的跨域对的实验结果表明，新的跨域分类方法明显优于几种流行的跨域情感分类方法。