{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:49:22Z","timestamp":1777704562287,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2020,8,27]],"date-time":"2020-08-27T00:00:00Z","timestamp":1598486400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,11,19]]},"abstract":"<jats:p>The purpose of sentiment classification is to solve the problem of automatic judgment of sentiment tendency. In the sentiment classification task of text data (such as online reviews), the traditional deep learning model focuses on algorithm optimization, but ignores the characteristics of the imbalanced distribution of the number of samples in each classification, which will cause the classification performance of the model to decrease in practical applications. In this paper, the experiment is divided into two stages. In the first stage, samples of minority class in the sample distribution are used to train a sequence generative adversarial nets, so that the sequence generative adversarial nets can learn the features of the samples of minority class in depth. In the second stage, the trained generator of sequence generative adversarial nets is used to generate false samples of minority class and mix them with the original samples to balance the sample distribution. After that, the mixed samples are input into the sentiment classification deep model to complete the model training. Experimental results show that the model has excellent classification performance in comparing a variety of deep learning models based on classic imbalanced learning methods in the sentiment classification task of hotel reviews.<\/jats:p>","DOI":"10.3233\/jifs-201370","type":"journal-article","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T10:17:10Z","timestamp":1599560230000},"page":"7909-7919","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Imbalanced sentiment classification based on sequence generative adversarial nets"],"prefix":"10.1177","volume":"39","author":[{"given":"Chuantao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China"},{"name":"Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing, China"}]},{"given":"Xuexin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China"},{"name":"Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing, China"}]},{"given":"Linkai","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China"},{"name":"Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing, China"}]}],"member":"179","published-online":{"date-parts":[[2020,8,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2016.31"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007733"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","unstructured":"YuL. ZhangW. and WangJ. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient arXiv: Learning (2016).","DOI":"10.1609\/aaai.v31i1.10804"},{"key":"e_1_3_2_5_2","first-page":"151","article-title":"Target-dependent Twitter sentiment Classification","volume":"1","author":"Jiang L.","year":"2011","unstructured":"JiangL., YuM., ZhouM., LiuX. and ZhaoT., Target-dependent Twitter sentiment Classification, Proc 49th Annu Meeting Assoc Comput Linguistics Hum Lang Technol 1 (2011), 151\u201316.","journal-title":"Proc 49th Annu Meeting Assoc Comput Linguistics Hum Lang Technol"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","unstructured":"KiritchenkoS. ZhuX. CherryC. and MohammadS. NRC-Canada-2014: Detecting aspects and sentiment in customer reviews Proc 8th Int Workshop Semantic Eval (SemEval) (2014) 437\u2013442.","DOI":"10.3115\/v1\/S14-2076"},{"key":"e_1_3_2_7_2","unstructured":"VoD.T. and ZhangY. Target-dependent twitter sentiment classification with rich automatic features Proc IJCAI (2015) 1347\u20131353."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165551517703514"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/info9120307"},{"key":"e_1_3_2_10_2","first-page":"79","article-title":"Thumbs up? Sentiment Classification using Machine Learning Techniques","volume":"10","author":"Pang B.","year":"2002","unstructured":"PangB., LeeL. and VaithyanathanS., Thumbs up? Sentiment Classification using Machine Learning Techniques, Empirical Methods in Natural Language Processing 10 (2002), 79\u201386.","journal-title":"Empirical Methods in Natural Language Processing"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","unstructured":"TkachenkoR. IzoninI. TkachenkoP. and DronyukI. Committee of the SGTM Neural-Like Structures with Extended Inputs for Predictive Analytics in Insurance In International Conference on Big Data Innovations and Applications (2019) 121\u2013132.","DOI":"10.1007\/978-3-030-27355-2_9"},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","unstructured":"TkachenkoR. TkachenkoP. IzoninI. VitynskyiP. KryvinskaN. and TsymbalY. Committee of the Combined RBF-SGTM Neural-Like Structures for Prediction Tasks In International Conference on Mobile Web and Intelligent Information Systems (2019) 267\u2013277.","DOI":"10.1007\/978-3-030-27192-3_21"},{"key":"e_1_3_2_13_2","unstructured":"RanaR. Emotion Classification from Noisy Speech - A Deep Learning Approach arXiv preprint arXiv:1603.05901 (2016)."},{"key":"e_1_3_2_14_2","unstructured":"DevlinJ. ChangM.W. LeeK. and ToutanovaK. Bert: Pre-training of deep bidirectional transformers for language understanding arXiv preprint arXiv: 1810.04805 (2018)."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJICT.2016.077687"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_3_2_17_2","unstructured":"HeH. ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning Neural Networks 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence) IEEE International Joint Conference on IEEE (2008)."},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","unstructured":"TkachenkoR. DoroshenkoA. IzoninI. TsymbalY. and HavryshB. Imbalance Data Classification via Neural-like Structures of Geometric Transformations Model: Local and Global Approaches In International Conference on Computer Science Engineering and Education Application (2018) 112\u2013122.","DOI":"10.1007\/978-3-319-91008-6_12"},{"key":"e_1_3_2_19_2","doi-asserted-by":"crossref","unstructured":"YanY. LiuY. and ShyuM. Utilizing concept correlations for effective imbalanced data classification Information Reuse and Integration (2014) 561\u2013568.","DOI":"10.1109\/IRI.2014.7051939"},{"key":"e_1_3_2_20_2","unstructured":"LiS. WangZ. and ZhouG. Semi-Supervised Learning for Imbalanced Sentiment Classification International Joint Conference on Ijcai (2011)."},{"key":"e_1_3_2_21_2","first-page":"2672","article-title":"Generative Adversarial Networks","volume":"3","author":"Goodfellow I.J.","year":"2014","unstructured":"GoodfellowI.J., Pouget-AbadieJ. and MirzaM., Generative Adversarial Networks, Advances in Neural Information Processing Systems 3 (2014), 2672\u20132680.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","unstructured":"BousmalisK. Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks Computer Vision and Pattern Recognition (2017) 95\u2013104.","DOI":"10.1109\/CVPR.2017.18"},{"key":"e_1_3_2_23_2","unstructured":"AntoniouA. StorkeyA. and EdwardsH. Data Augmentation Generative Adversarial Networks arXiv: Machine Learning (2017)."},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","unstructured":"ChoK. MerrienboerB.V. and GulcehreC. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Computer Science (2014).","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature16961"},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","unstructured":"KrawczykB. McInnesB.T. and CanoA. Sentiment classification from multi-class imbalanced twitter data using binarization International Conference on Hybrid Artificial Intelligence Systems Springer Cham (2017) 26\u201337.","DOI":"10.1007\/978-3-319-59650-1_3"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","unstructured":"LiS. ZhouG. WangZ. LeeS.Y.M. and WangR. Imbalanced sentiment classification Proceedings of the 20thACM International Conference on Information and Knowledge Management (2011) 2469\u20132472.","DOI":"10.1145\/2063576.2063994"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-015-9319-y"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.06.019"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.12.035"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007735"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-201370","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-201370","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-201370","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:29Z","timestamp":1777455689000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-201370"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,27]]},"references-count":30,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,11,19]]}},"alternative-id":["10.3233\/JIFS-201370"],"URL":"https:\/\/doi.org\/10.3233\/jifs-201370","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,27]]}}}