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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2020,9,30]]},"abstract":"<jats:p>\n            Sentiment classification aims to detect polarity from a piece of text. The polarity is usually positive or negative, and the text genre is usually product review. The challenges of sentiment classification are that it is hard to capture semantic of reviews, and the labeled data is hard to annotate. Therefore, we propose\n            <jats:italic>neural co-training<\/jats:italic>\n            to learn the semantic representation of each review using the neural network model, and learn the information from unlabeled data using a co-training framework. In particular, we use the attention-based bi-directional Gated Recurrent Unit (Att-BiGRU) to model the semantic content of each review and regard different categories of the target product as different views. We then use a co-training framework to learn and predict the unlabeled reviews with different views. Experiment results with the Yelp dataset demonstrate the effectiveness of our approach.\n          <\/jats:p>","DOI":"10.1145\/3394113","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T12:39:07Z","timestamp":1594125547000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Neural Co-training for Sentiment Classification with Product Attributes"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8728-8208","authenticated-orcid":false,"given":"Ruirui","family":"Bai","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongqing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7102-0143","authenticated-orcid":false,"given":"Fang","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shoushan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guodong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00636"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, June 15--20","author":"Andreevskaia Alina","year":"2008","unstructured":"Alina Andreevskaia and Sabine Bergler . 2008 . When specialists and generalists work together: Overcoming domain dependence in sentiment tagging . In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, June 15--20 , 2008, Columbus, Ohio. 290--298. Alina Andreevskaia and Sabine Bergler. 2008. When specialists and generalists work together: Overcoming domain dependence in sentiment tagging. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, June 15--20, 2008, Columbus, Ohio. 290--298."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, June 23--30","author":"Blitzer John","year":"2007","unstructured":"John Blitzer , Mark Dredze , and Fernando Pereira . 2007 . Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification . 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