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In this research, we propose a sentiment dictionary for Vietnamese, known as VNSD, which adopts machine translation, Logistic Regression, \u201cdouble propagation,\u201d and fuzzy rules. The sentiment dictionary counts approximately 5,000 adjectives, 2,000 verbs, 300 nouns, and more than 200 adverbs, which lead to a combination of more than 100,000 sentiment phrases covering the Vietnamese emotional lexicons. We found that a hybrid approach offers better performance compared to stand-alone data-mining or natural language processing techniques. The key contribution of VNSD that distinguishes it from related work is a deep investment in exploiting Vietnamese linguistic characteristics to propose suitable rules for computing the sentiment scores of Vietnamese text phrases.<\/jats:p>","DOI":"10.3233\/jifs-172053","type":"journal-article","created":{"date-parts":[[2018,7,10]],"date-time":"2018-07-10T14:40:04Z","timestamp":1531233604000},"page":"967-978","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":17,"title":["A hybrid approach for building a Vietnamese sentiment dictionary"],"prefix":"10.1177","volume":"35","author":[{"given":"Thien Khai","family":"Tran","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology - VNU-HCM, Ho Chi Minh City, Vietnam"},{"name":"Faculty of Information Technology, Ho Chi Minh City University of Foreign Languages and Information Technology, Ho Chi Minh City, Vietnam"}]},{"given":"Tuoi Thi","family":"Phan","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology - VNU-HCM, Ho Chi Minh City, Vietnam"}]}],"member":"179","published-online":{"date-parts":[[2018,7,9]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-5010"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.03.040"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.06.015"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-017-0032-9"},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","unstructured":"HuM. 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