{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T07:36:08Z","timestamp":1780817768847,"version":"3.54.1"},"reference-count":44,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T00:00:00Z","timestamp":1606867200000},"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 Information Science"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>With the advent of deep neural models in natural language processing tasks, having a large amount of training data plays an essential role in achieving accurate models. Creating valid training data, however, is a challenging issue in many low-resource languages. This problem results in a significant difference between the accuracy of available natural language processing tools for low-resource languages compared with rich languages. To address this problem in the sentiment analysis task in the Persian language, we propose a cross-lingual deep learning framework to benefit from available training data of English. We deployed cross-lingual embedding to model sentiment analysis as a transfer learning model which transfers a model from a rich-resource language to low-resource ones. Our model is flexible to use any cross-lingual word embedding model and any deep architecture for text classification. Our experiments on English Amazon dataset and Persian Digikala dataset using two different embedding models and four different classification networks show the superiority of the proposed model compared with the state-of-the-art monolingual techniques. Based on our experiment, the performance of Persian sentiment analysis improves 22% in static embedding and 9% in dynamic embedding. Our proposed model is general and language-independent; that is, it can be used for any low-resource language, once a cross-lingual embedding is available for the source\u2013target language pair. Moreover, by benefitting from word-aligned cross-lingual embedding, the only required data for a reliable cross-lingual embedding is a bilingual dictionary that is available between almost all languages and the English language, as a potential source language.<\/jats:p>","DOI":"10.1177\/0165551520962781","type":"journal-article","created":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T05:12:06Z","timestamp":1606885926000},"page":"449-462","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":38,"title":["Deep Persian sentiment analysis: Cross-lingual training for low-resource languages"],"prefix":"10.1177","volume":"48","author":[{"given":"Rouzbeh","family":"Ghasemi","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Amirkabir University of Technology, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Arad","family":"Ashrafi Asli","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Amirkabir University of Technology, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8110-1342","authenticated-orcid":false,"given":"Saeedeh","family":"Momtazi","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Amirkabir University of Technology, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2020,12,2]]},"reference":[{"key":"bibr1-0165551520962781","doi-asserted-by":"publisher","DOI":"10.1002\/asi.21416"},{"key":"bibr2-0165551520962781","doi-asserted-by":"publisher","DOI":"10.1002\/asi.21662"},{"key":"bibr3-0165551520962781","first-page":"1215","volume-title":"Proceedings of the eighth international conference on language resources and evaluation (LREC\u201912)","author":"Momtazi S"},{"key":"bibr4-0165551520962781","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.10.030"},{"key":"bibr5-0165551520962781","first-page":"2855","volume-title":"Proceedings of the 12th language resources and evaluation conference","author":"Asli SAA"},{"key":"bibr6-0165551520962781","volume-title":"Proceedings of International Conference on Machine Learning, Lille","author":"Gouws S"},{"key":"bibr7-0165551520962781","first-page":"5012","volume-title":"Proceedings of the thirty-second AAAI conference on artificial intelligence (AAAI-18)","author":"Artetxe M"},{"key":"bibr8-0165551520962781","first-page":"216","volume-title":"The 16th CSI international symposium on artificial intelligence and signal processing (AISP 2012)","author":"Shams M"},{"key":"bibr9-0165551520962781","first-page":"36","volume-title":"2017 International symposium on computer Science and software engineering conference (CSSE)","author":"Basiri ME"},{"key":"bibr10-0165551520962781","volume-title":"1st International conference on new research achievements in electrical and computer engineering (ICNRAECE)","author":"Basiri M"},{"key":"bibr11-0165551520962781","first-page":"1","volume-title":"2013 21st Iranian conference on electrical engineering (ICEE)","author":"Bagheri A"},{"key":"bibr12-0165551520962781","first-page":"303","volume-title":"International conference on application of natural language to information systems","author":"Saraee M"},{"key":"bibr13-0165551520962781","doi-asserted-by":"publisher","DOI":"10.1145\/3195633"},{"key":"bibr14-0165551520962781","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/361201"},{"key":"bibr15-0165551520962781","doi-asserted-by":"publisher","DOI":"10.2174\/9781681085029117010009"},{"key":"bibr16-0165551520962781","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.05.018"},{"key":"bibr17-0165551520962781","first-page":"135","volume":"3","author":"Alimardani S","year":"2015","journal-title":"J Inf Syst Telecommun"},{"key":"bibr18-0165551520962781","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-017-9513-1"},{"key":"bibr19-0165551520962781","doi-asserted-by":"publisher","DOI":"10.29252\/jsdp.15.1.71"},{"key":"bibr20-0165551520962781","unstructured":"Sabeti B, Hosseini P, Ghassem-Sani G, et al. 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