{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:15:00Z","timestamp":1777706100768,"version":"3.51.4"},"reference-count":34,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"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":[[2022,6,9]]},"abstract":"<jats:p>Sentiment classification is one of the major tasks of natural language processing (NLP) and has gained much attention by researchers and businesses in recent years. However, the semantics of the social networking language is becoming increasingly complex and unpredictable, affecting the accuracy of the associated NLP systems. In this paper, we propose a hybrid sentiment analysis (SA) framework that classifies the opinions of Vietnamese reviews into one of two types: positive or negative. The special feature of the proposed framework is that it is built on a combination of three different text representation models that focus on analyzing social media network language characteristics. Our system achieved an accuracy score of 81.54% on the test set, which is better than other strategies. Based on the experimental results, this work proves that the choice of text representation model determines the performance of the system.<\/jats:p>","DOI":"10.3233\/jifs-219278","type":"journal-article","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T11:46:04Z","timestamp":1646999164000},"page":"1771-1777","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Building an enhanced sentiment classification framework based on natural language processing"],"prefix":"10.1177","volume":"43","author":[{"given":"Thien Khai","family":"Tran","sequence":"first","affiliation":[{"name":"Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam"},{"name":"Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam"},{"name":"Ho Chi Minh City University of Foreign Languages-Information Technology (HUFLIT), Ho Chi Minh City, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoa Minh","family":"Dinh","sequence":"additional","affiliation":[{"name":"Ho Chi Minh City University of Foreign Languages-Information Technology (HUFLIT), Ho Chi Minh City, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuoi Thi","family":"Phan","sequence":"additional","affiliation":[{"name":"Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam"},{"name":"Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","unstructured":"TurneyP.D. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews in Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL \u201902 (2001) 417 doi: 10.3115\/1073083.1073153.","DOI":"10.3115\/1073083.1073153"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1162\/COLI_a_00049"},{"key":"e_1_3_2_4_2","unstructured":"Yichun YinM.Z. and Yangqiu Song Document-levelmultiaspect sentiment classification as machine comprehension in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017) 2044\u20132054."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","unstructured":"ZhouX. WanX. and XiaoJ. Attention-based LSTM Network for Cross-Lingual Sentiment Classification in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (2016) 247\u2013256 doi: 10.18653\/v1\/D16-1024.","DOI":"10.18653\/v1\/D16-1024"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2885032"},{"key":"e_1_3_2_8_2","unstructured":"MikolovT. ChenK. CorradoG. and DeanJ. Efficient Estimation of Word Representations in Vector Space in International Conference on Learning Representations 2013."},{"key":"e_1_3_2_9_2","unstructured":"DevlinL.K.J and ChangM.W. BERT: pre-training of deep bidirectional transformers for language understanding in Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2019) 4171\u20134186."},{"key":"e_1_3_2_10_2","doi-asserted-by":"crossref","unstructured":"NguyenD.Q. and NguyenA.T. PhoBERT: Pre-trained language models for Vietnamese 2020.","DOI":"10.18653\/v1\/2020.findings-emnlp.92"},{"key":"e_1_3_2_11_2","unstructured":"DumaisS. FurnasG. LandauerT. and DeerwesterS. Latent semantic indexing in Proceedings of the Text Retrieval Conference 1995 1995."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007617005950"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_3_2_14_2","unstructured":"MnihA. and HintG. A scalable hierarchical distributed language model | Proceedings of the 21st International Conference on Neural Information Processing Systems in NIPS\u201908: Proceedings of the 21st International Conference on Neural Information Processing Systems (2008) 1081\u20131088."},{"key":"e_1_3_2_15_2","unstructured":"MikolovT. SutskeverI. ChenK. CorradoG. and DeanJ. Distributed representations of words and phrases and their compositionality Adv. Neural Inf. Process. Syst. (2013) 3111\u20133119."},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","unstructured":"PenningtonJ. SocherR. and ManningC.D. GloVe: Global vectors for word representation in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014) 1532\u20131543 doi: 10.3115\/v1\/d14-1162.","DOI":"10.3115\/v1\/d14-1162"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.2200\/S00416ED1V01Y201204HLT016"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-179021"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.3233\/his-200285"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-179383"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169958"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.3390\/app9132760"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3004180"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-179881"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-179417"},{"key":"e_1_3_2_26_2","unstructured":"DurantiA. and GoodwinC. Rethinking context language interactive phenomenon 11. Cambridge University Press 1992."},{"key":"e_1_3_2_27_2","unstructured":"KhangN.V. Ng\u00f4n Ng\u0169 Ma. ng - Bi\u00ean Th Ngg\u00f4n Ng\u0169 Tr\u00ean Ma. ng Ti\u00eang Vi\u00ea. t (Social Networking Language). Vinabook JSC 2019."},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s007999900025"},{"key":"e_1_3_2_29_2","unstructured":"FrankW. StefanKoppenL. Mathieu Noordman and Leo VonkG. M. Modeling multiple levels of text representation. Mahwah NJ : Erlbaum 2007."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","unstructured":"VuX.S. VuT. TranN.S. and JiangL. ETNLP: A visual aided systematic approach to select pre-trained embeddings for a downstream task in International Conference Recent Advances in Natural Language Processing RANLP 2019-Septe (2019) 1285\u20131294 doi: 10.26615\/978-954-452-056-4_47.","DOI":"10.26615\/978-954-452-056-4_47"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","unstructured":"BoserB.E. GuyonI.M. and VapnikV.N. A training algorithm for optimal margin classifiers in Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory (1992) 144\u2013152 doi: 10.1145\/130385.130401.","DOI":"10.1145\/130385.130401"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9574.1988.tb01238.x"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.159058"},{"key":"e_1_3_2_34_2","unstructured":"YoavF. and RobertE.S. Experiments with a new boosting algorithm | Proceedings of the Thirteenth International Conference on International Conference on Machine Learning in ICML\u201996: Proceedings of the Thirteenth International Conference on International Conference onMachine Learning (1996) 148\u2013156."},{"key":"e_1_3_2_35_2","unstructured":"LeQ. and MikolovT. Distributed representations of sentences and documents in ICML\u201914: Proceedings of the 31st International Conference on International Conference on Machine Learning (2014) 1188\u20131196."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-219278","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-219278","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-219278","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:46:01Z","timestamp":1777455961000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-219278"}},"subtitle":[],"editor":[{"given":"Valentina Emilia","family":"Balas","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,3,9]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,6,9]]}},"alternative-id":["10.3233\/JIFS-219278"],"URL":"https:\/\/doi.org\/10.3233\/jifs-219278","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,9]]}}}