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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.<\/jats:p>","DOI":"10.1177\/01655515211012329","type":"journal-article","created":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T00:51:40Z","timestamp":1620953500000},"page":"567-581","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["A novel scheme of domain transfer in document-level cross-domain sentiment classification"],"prefix":"10.1177","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8267-2801","authenticated-orcid":false,"given":"Yueting","family":"Lei","sequence":"first","affiliation":[{"name":"China Institute of Quality Research, China; Department of Industrial Engineering, Shanghai Jiao Tong University, China"}]},{"given":"Yanting","family":"Li","sequence":"additional","affiliation":[{"name":"China Institute of Quality Research, China; Department of Industrial Engineering, Shanghai Jiao Tong University, China"}]}],"member":"179","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"key":"bibr1-01655515211012329","first-page":"417","volume-title":"Proceedings of the 40th annual meeting on Association for Computational Linguistics","author":"Turney PD"},{"key":"bibr2-01655515211012329","first-page":"491","volume-title":"Proceedings of the 12th European Conference on machine learning","author":"Turney PD"},{"key":"bibr3-01655515211012329","first-page":"79","volume-title":"Proceedings of the conference on empirical methods in natural language processing","author":"Pang B"},{"key":"bibr4-01655515211012329","first-page":"701","volume-title":"Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP","author":"Dasgupta S"},{"key":"bibr5-01655515211012329","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.03.045"},{"key":"bibr6-01655515211012329","doi-asserted-by":"publisher","DOI":"10.1177\/0165551515613226"},{"key":"bibr7-01655515211012329","first-page":"1826","volume-title":"Proceedings of the 21nd international joint conference on artificial intelligence","author":"Li S"},{"key":"bibr8-01655515211012329","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2017.02.008"},{"key":"bibr9-01655515211012329","first-page":"1371","volume-title":"Proceedings of the 24th AAAI conference on artificial intelligence","author":"Li F"},{"key":"bibr10-01655515211012329","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/2497471"},{"key":"bibr11-01655515211012329","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2945911"},{"key":"bibr12-01655515211012329","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106743"},{"key":"bibr13-01655515211012329","unstructured":"Li W, Shao W, Ji S et al. 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