{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T22:08:16Z","timestamp":1779314896734,"version":"3.51.4"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2018,4,20]],"date-time":"2018-04-20T00:00:00Z","timestamp":1524182400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1007\/s00500-018-3187-9","type":"journal-article","created":{"date-parts":[[2018,4,20]],"date-time":"2018-04-20T07:47:21Z","timestamp":1524210441000},"page":"5431-5442","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Enhanced cross-domain sentiment classification utilizing a multi-source transfer learning approach"],"prefix":"10.1007","volume":"23","author":[{"given":"Farhan Hassan","family":"Khan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Usman","family":"Qamar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saba","family":"Bashir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,4,20]]},"reference":[{"key":"3187_CR1","unstructured":"Ash JT, Schapire RE (2016) Multi-source domain adaptation using approximate label matching. arXiv preprint arXiv:1602.04889"},{"key":"3187_CR2","first-page":"2200","volume":"10","author":"S Baccianella","year":"2010","unstructured":"Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. LREC 10:2200\u20132204","journal-title":"LREC"},{"key":"3187_CR3","unstructured":"Balahur A (2013) Sentiment analysis in social media texts. In: 4th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 120\u2013128"},{"key":"3187_CR4","unstructured":"Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL, vol 7, pp 440\u2013447"},{"issue":"8","key":"3187_CR5","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1109\/TKDE.2012.103","volume":"25","author":"D Bollegala","year":"2013","unstructured":"Bollegala D, Weir D, Carroll J (2013) Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Trans Knowl Data Eng 25(8):1719\u20131731","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"3187_CR6","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1109\/TKDE.2015.2475761","volume":"28","author":"D Bollegala","year":"2016","unstructured":"Bollegala D, Mu T, Goulermas JY (2016) Cross-domain sentiment classification using sentiment sensitive embeddings. IEEE Trans Knowl Data Eng 28(2):398\u2013410","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"3187_CR7","first-page":"18","volume":"6","author":"R Chattopadhyay","year":"2012","unstructured":"Chattopadhyay R, Sun Q, Fan W, Davidson I, Panchanathan S, Ye J (2012) Multisource domain adaptation and its application to early detection of fatigue. ACM Trans Knowl Discov Data (TKDD) 6(4):18","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"3187_CR8","first-page":"719","volume-title":"Analysis of co-training algorithm with very small training sets","author":"L Didaci","year":"2012","unstructured":"Didaci L, Fumera G, Gimel\u2019farb Roli F, Hancock E, Imiya A, Kuijper A, Kudo M, Omachi S, Windeatt T, Yamada K (2012) Analysis of co-training algorithm with very small training sets. Springer, Berlin, pp 719\u2013726"},{"key":"3187_CR9","doi-asserted-by":"crossref","unstructured":"Domeniconi G, Moro G, Pagliarani A, Pasolini R (2015) Markov chain based method for in-domain and cross-domain sentiment classification. In: Proceedings of the 7th international conference on knowledge discovery and information retrieval","DOI":"10.5220\/0005636001270137"},{"key":"3187_CR10","doi-asserted-by":"crossref","unstructured":"Duan L, Tsang IW, Xu D, Chua TS (2009) Domain adaptation from multiple sources via auxiliary classifiers. In: Proceedings of the 26th annual international conference on machine learning, ACM, New York, pp 289\u2013296","DOI":"10.1145\/1553374.1553411"},{"key":"3187_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/3057481","volume":"2016","author":"N Fazakis","year":"2016","unstructured":"Fazakis N, Karlos S, Kotsiantis S, Sgarbas K (2016) Self-trained LMT for semisupervised learning. Comput Intel Neurosci 2016:1\u201313","journal-title":"Comput Intel Neurosci"},{"key":"3187_CR12","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.knosys.2015.05.020","volume":"86","author":"M Franco-Salvador","year":"2015","unstructured":"Franco-Salvador M, Cruz FL, Troyano JA, Rosso P (2015) Cross-domain polarity classification using a knowledge-enhanced meta-classifier. Knowl Based Syst 86:46\u201356","journal-title":"Knowl Based Syst"},{"key":"3187_CR13","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-319-18458-6_3","volume-title":"Advances in social media analysis","author":"G Gezici","year":"2015","unstructured":"Gezici G, Yanikoglu B, Tapucu D, Sayg\u0131n Y (2015) Sentiment analysis using domain-adaptation and sentence-based analysis. In: Gaber MM, Cocea M, Wiratunga N, Goker A (eds) Advances in social media analysis. Springer, Berlin, pp 45\u201364"},{"key":"3187_CR14","doi-asserted-by":"crossref","unstructured":"Huang X, Rao Y, Xie H, Wong TL, Wang FL (2017) Cross-domain sentiment classification via topic-related TrAdaBoost. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, pp 4939\u20134940","DOI":"10.1609\/aaai.v31i1.11099"},{"key":"3187_CR15","first-page":"169","volume-title":"Advances in kernel methods\u2014support vector learning","author":"T Joachims","year":"1998","unstructured":"Joachims T (1998) Making large-scale SVM learning practical. In: Sch\u00f6lkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods\u2014support vector learning. MIT Press, Cambridge, pp 169\u2013184"},{"key":"3187_CR16","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.3233\/IFS-151658","volume":"29","author":"FH Khan","year":"2015","unstructured":"Khan FH, Qamar U, Bashir S (2015) Building normalized SentiMI to enhance semi-supervised sentiment analysis. J Intel Fuzzy Syst 29:1805\u20131816","journal-title":"J Intel Fuzzy Syst"},{"key":"3187_CR17","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1016\/j.ins.2016.07.028","volume":"367","author":"FH Khan","year":"2016","unstructured":"Khan FH, Qamar U, Bashir S (2016) eSAP: a decision support framework for enhanced sentiment analysis and polarity classification. Inf Sci 367:862\u2013873","journal-title":"Inf Sci"},{"issue":"3","key":"3187_CR18","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1007\/s10115-016-0993-1","volume":"51","author":"FH Khan","year":"2017","unstructured":"Khan FH, Qamar U, Bashir S (2017) A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet. Knowl Inf Syst 51(3):851\u2013872","journal-title":"Knowl Inf Syst"},{"issue":"2","key":"3187_CR19","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1016\/j.eswa.2013.07.097","volume":"41","author":"K Kim","year":"2014","unstructured":"Kim K, Chung BS, Choi Y, Lee S, Jung JY, Park J (2014) Language independent semantic kernels for short-text classification. Expert Syst Appl 41(2):735\u2013743","journal-title":"Expert Syst Appl"},{"key":"3187_CR20","unstructured":"Li S, Xue Y, Wang Z, Zhou G (2013) Active learning for cross-domain sentiment classification. In: IJCAI"},{"key":"3187_CR21","unstructured":"Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies"},{"key":"3187_CR22","doi-asserted-by":"crossref","unstructured":"Mahalakshmi S, Sivasankar E (2015) Cross domain sentiment analysis using different machine learning techniques. In: Proceedings of the fifth international conference on fuzzy and neuro computing (FANCCO-2015), Springer, Berlin, pp 77\u201387","DOI":"10.1007\/978-3-319-27212-2_7"},{"key":"3187_CR23","unstructured":"Mansour Y, Mohri M, Rostamizadeh A (2009) Domain adaptation with multiple sources. In: Advances in neural information processing systems, pp 1041\u20131048"},{"key":"3187_CR24","doi-asserted-by":"crossref","unstructured":"Mao K, Niu J, Wang X, Wang L, Qiu M (2015) Cross-domain sentiment analysis of product reviews by combining lexicon-based and learn-based techniques. In: 2015 IEEE 17th international conference on high performance computing and communications (HPCC), 2015 IEEE 7th international symposium on cyberspace safety and security (CSS), 2015 IEEE 12th international conference on embedded software and systems (ICESS), pp 351\u2013356","DOI":"10.1109\/HPCC-CSS-ICESS.2015.75"},{"issue":"4","key":"3187_CR25","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1016\/j.asej.2014.04.011","volume":"5","author":"W Medhat","year":"2014","unstructured":"Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093\u20131113","journal-title":"Ain Shams Eng J"},{"issue":"11","key":"3187_CR26","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39\u201341","journal-title":"Commun ACM"},{"key":"3187_CR27","unstructured":"Moore A, Rayson P, Young S (2016) Domain adaptation using stock market prices to refine sentiment dictionaries. In: Proceedings of the 10th edition of language resources and evaluation conference (LREC2016). European Language Resources Association (ELRA)"},{"issue":"2","key":"3187_CR28","first-page":"96","volume":"22","author":"MY Pak","year":"2016","unstructured":"Pak MY, Gunal S (2016) Sentiment classification based on domain prediction. Elektron Elektrotech 22(2):96\u201399","journal-title":"Elektron Elektrotech"},{"key":"3187_CR29","doi-asserted-by":"crossref","unstructured":"Pan, SJ, Ni X, Sun JT, Yang Q, Chen Z (2010) Cross-domain sentiment classification via spectral feature alignment. In: Proceedings of the 19th international conference on world wide web, ACM, New York, pp 751\u2013760","DOI":"10.1145\/1772690.1772767"},{"key":"3187_CR30","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.knosys.2015.09.017","volume":"90","author":"J Pan","year":"2015","unstructured":"Pan J, Hu X, Zhang Y, Li P, Lin Y, Li H, Li L (2015) Quadruple transfer learning exploiting both shared and non-shared concepts for text classification. Knowl Based Syst 90:199\u2013210","journal-title":"Knowl Based Syst"},{"key":"3187_CR31","doi-asserted-by":"crossref","unstructured":"Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on association for computational linguistics","DOI":"10.3115\/1218955.1218990"},{"key":"3187_CR32","unstructured":"Seah CW, Chieu HL, Chai KMA, Teow N, Yeong LW (2015) Troll detection by domain-adapting sentiment analysis. In: 18th International conference on information fusion (Fusion)"},{"key":"3187_CR33","unstructured":"Shinnou H, Xiao L, Sasaki M, Komiya K (2015) Hybrid method of semi-supervised learning and feature weighted learning for domain adaptation of document classification. In: Proceedings of the 29th pacific asia conference on language, information and computation, pp 496\u2013503"},{"issue":"3","key":"3187_CR34","first-page":"491","volume":"18","author":"G Sidorov","year":"2014","unstructured":"Sidorov G, Gelbukh A, G\u00f3mez-Adorno H, Pinto D (2014) Soft similarity and soft cosine measure: similarity of features in vector space model. Comput Syst 18(3):491\u2013504","journal-title":"Comput Syst"},{"key":"3187_CR35","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.ins.2014.04.034","volume":"285","author":"J Smailovi\u0107","year":"2014","unstructured":"Smailovi\u0107 J, Gr\u010dar M, Lavra\u010d N, \u017dnidar\u0161i\u010d M (2014) Stream-based active learning for sentiment analysis in the financial domain. Inf Sci 285:181\u2013203","journal-title":"Inf Sci"},{"key":"3187_CR36","doi-asserted-by":"crossref","unstructured":"Toutanova K, Klein D, Manning CD, Singer Y (2003) Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 conference of the north american chapter of the association for computational linguistics on human language technology","DOI":"10.3115\/1073445.1073478"},{"issue":"2","key":"3187_CR37","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10115-013-0706-y","volume":"42","author":"I Triguero","year":"2013","unstructured":"Triguero I, Garc\u00eda S, Herrera Francisco (2013) Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study. Knowl Inf Syst 42(2):245\u2013284","journal-title":"Knowl Inf Syst"},{"key":"3187_CR38","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.jnca.2017.11.001","volume":"101","author":"L Wang","year":"2018","unstructured":"Wang L, Niu J, Song H, Atiquzzaman M (2018) SentiRelated: a cross-domain sentiment classification algorithm for short texts through sentiment related index. J Netw Comput Appl 101:111\u2013119","journal-title":"J Netw Comput Appl"},{"key":"3187_CR39","doi-asserted-by":"crossref","unstructured":"Wu F, Huang Y (2016) Sentiment domain adaptation with multiple sources. In: Proceedings of the 54th annual meeting on association for computational linguistics, pp 301\u2013310","DOI":"10.18653\/v1\/P16-1029"},{"issue":"1","key":"3187_CR40","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/TMM.2014.2375793","volume":"17","author":"X Yang","year":"2015","unstructured":"Yang X, Zhang T, Xu C (2015) Cross-domain feature learning in multimedia. IEEE Trans Multimed 17(1):64\u201378","journal-title":"IEEE Trans Multimed"},{"key":"3187_CR41","doi-asserted-by":"crossref","unstructured":"Yang L, Zhang S, Lin H, Wei X (2015) Incorporating sample filtering into subject-based ensemble model for cross-domain sentiment classification. In: Chinese computational linguistics and natural language processing based on naturally annotated big data, Springer, Berlin, pp 116\u2013127","DOI":"10.1007\/978-3-319-25816-4_10"},{"key":"3187_CR42","doi-asserted-by":"crossref","unstructured":"Yoshida Y, Hirao T, Iwata T, Nagata M, Matsumoto Y (2011) Transfer learning for multiple-domain sentiment analysis-identifying domain dependent\/independent word polarity. In: Proceedings of the twenty-fifth AAAI conference on artificial intelligence, pp 1286\u20131291","DOI":"10.1609\/aaai.v25i1.8081"},{"key":"3187_CR43","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.patrec.2015.07.006","volume":"65","author":"Y Zhang","year":"2015","unstructured":"Zhang Y, Hu X, Li P, Li L, Wu X (2015a) Cross-domain sentiment classification-feature divergence, polarity divergence or both? Pattern Recognit Lett 65:44\u201350","journal-title":"Pattern Recognit Lett"},{"key":"3187_CR44","doi-asserted-by":"crossref","unstructured":"Zhang S, Liu H, Yang L, Lin H (2015b) A cross-domain sentiment classification method based on extraction of key sentiment sentence. In: National CCF conference on natural language processing and chinese computing, Springer, Berlin, pp 90\u2013101","DOI":"10.1007\/978-3-319-25207-0_8"},{"key":"3187_CR45","doi-asserted-by":"crossref","unstructured":"Zhang Y, Xu X, Hu X (2015c) A common subspace construction method in cross-domain sentiment classification. In: International conference on electronic science and automation control (ESAC). Atlantis Press, Amsterdam. pp 48\u201352","DOI":"10.2991\/esac-15.2015.13"},{"key":"3187_CR46","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.neucom.2014.12.006","volume":"159","author":"G Zhou","year":"2015","unstructured":"Zhou G, Zhou Y, Guo X, Tu X, He T (2015) Cross-domain sentiment classification via topical correspondence transfer. Neurocomputing 159:298\u2013305","journal-title":"Neurocomputing"},{"key":"3187_CR47","doi-asserted-by":"crossref","unstructured":"Zhu E, Huang G, Mo B, Wu Q (2016) Features extraction based on neural network for cross-domain sentiment classification. In: International conference on database systems for advanced applications, Springer, Berlin, pp 81\u201388","DOI":"10.1007\/978-3-319-32055-7_7"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-018-3187-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-018-3187-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-018-3187-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T11:18:39Z","timestamp":1660994319000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-018-3187-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,20]]},"references-count":47,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2019,7]]}},"alternative-id":["3187"],"URL":"https:\/\/doi.org\/10.1007\/s00500-018-3187-9","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,20]]},"assertion":[{"value":"20 April 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"All the authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}