{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:21:24Z","timestamp":1773786084488,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2016,9,20]],"date-time":"2016-09-20T00:00:00Z","timestamp":1474329600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2017,6]]},"DOI":"10.1007\/s10115-016-0993-1","type":"journal-article","created":{"date-parts":[[2016,9,20]],"date-time":"2016-09-20T04:17:37Z","timestamp":1474345057000},"page":"851-872","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet"],"prefix":"10.1007","volume":"51","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":[[2016,9,20]]},"reference":[{"key":"993_CR1","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.asoc.2015.11.016","volume":"39","author":"FH Khan","year":"2016","unstructured":"Khan FH, Qamar U, Bashir S (2016) SentiMI: introducing point-wise mutual information with SentiWordNet to improve sentiment polarity detection. Appl Soft Comput 39:140\u2013153","journal-title":"Appl Soft Comput"},{"key":"993_CR2","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":"993_CR3","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1016\/j.ipm.2014.10.002","volume":"51","author":"MD Molina-Gonz\u00e1lez","year":"2015","unstructured":"Molina-Gonz\u00e1lez MD, Mart\u00ednez-C\u00e1mara E, Mart\u00edn-Valdivia MT, Ure\u00f1a-L\u00f3pez LA (2015) A Spanish semantic orientation approach to domain adaptation for polarity classification. Inf Process Manag 51:520\u2013531","journal-title":"Inf Process Manag"},{"key":"993_CR4","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.dss.2013.09.004","volume":"57","author":"FH Khan","year":"2014","unstructured":"Khan FH, Bashir S, Qamar U (2014) TOM: twitter opinion mining framework using hybrid classification scheme. Decis Support Syst 57:245\u2013257","journal-title":"Decis Support Syst"},{"key":"993_CR5","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 Intell Fuzzy Syst 29:1805\u20131816","journal-title":"J Intell Fuzzy Syst"},{"issue":"4","key":"993_CR6","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":"2","key":"993_CR7","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10115-013-0706-y","volume":"42","author":"Isaac Triguero","year":"2013","unstructured":"Triguero Isaac, Garc\u00eda Salvador, 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":"993_CR8","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 Intell Neurosci 2016:1\u201313","journal-title":"Comput Intell Neurosci"},{"key":"993_CR9","doi-asserted-by":"publisher","unstructured":"Didaci L, Fumera G, Roli F, Gimel\u2019farb, Hancock E, Imiya A, Kuijper A, Kudo M, Omachi S, Windeatt T, Yamada K (eds) (2012) Analysis of co-training algorithm with very small training sets. LNCS. Springer, Berlin Heidelberg. pp 719\u2013726. ISBN 9783642341656","DOI":"10.1007\/978-3-642-34166-3_79"},{"key":"993_CR10","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1016\/j.ipm.2015.05.006","volume":"51","author":"I Habernal","year":"2015","unstructured":"Habernal I, Pt\u00e1\u010dek T, Steinberger J (2015) Reprint of \u201dSupervised sentiment analysis in Czech social media\u201d. Inf Process Manag 51:532\u2013546","journal-title":"Inf Process Manag"},{"key":"993_CR11","doi-asserted-by":"publisher","unstructured":"Lin Y, Zhang J, Wang X, Zhou A (2012) An information theoretic approach to sentiment polarity classification. In: Proceedings of the 2nd joint WICOW\/AIRWeb workshop on web quality, pp 35\u201340","DOI":"10.1145\/2184305.2184313"},{"issue":"1","key":"993_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.5121\/ijsc.2014.5102","volume":"5","author":"PK Singh","year":"2014","unstructured":"Singh PK, Husain MS (2014) Methodological study of opinion mining and sentiment analysis techniques. Int J Soft Comput 5(1):11","journal-title":"Int J Soft Comput"},{"key":"993_CR13","unstructured":"Ortega R, Fonseca A, Montoyo A (2013) SSA-UO: unsupervised Twitter sentiment analysis. In: Second joint conference on lexical and computational semantics (*SEM), Vol. 2, pp 501\u2013507"},{"key":"993_CR14","unstructured":"Ohana B, Tierney B (2009) Sentiment classification of reviews using SentiWordNet. In: 9th. IT & T conference p 13"},{"key":"993_CR15","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.procs.2015.02.112","volume":"46","author":"J Bhaskar","year":"2015","unstructured":"Bhaskar J, Sruthi K, Nedungadi P (2015) Hybrid approach for emotion classification of audio conversation based on text and speech mining. Procedia Comput Sci 46:635\u2013643","journal-title":"Procedia Comput Sci"},{"key":"993_CR16","doi-asserted-by":"publisher","unstructured":"Chikersal P, Poria S, Cambria E, Gelbukh A, Siong CE (2015) Modelling public sentiment in twitter: using linguistic patterns to enhance supervised learning. In: Computational linguistics and intelligent text processing, Springer International Publishing, pp 49\u201365","DOI":"10.1007\/978-3-319-18117-2_4"},{"key":"993_CR17","doi-asserted-by":"publisher","unstructured":"Pandarachalil R, Sendhilkumar S, Mahalakshmi GS (2015) Twitter sentiment analysis for large-scale data: an unsupervised approach. In: Cognitive computation pp 1\u20139","DOI":"10.1007\/s12559-014-9310-z"},{"key":"993_CR18","unstructured":"Ghosh M, Kar A (2013) Unsupervised linguistic approach for sentiment classification from online reviews using SentiWordNet 3.0. Int J Eng Res Technol 2(9) ESRSA Publications"},{"key":"993_CR19","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/7287.001.0001","volume-title":"WordNet: an electronic database","author":"C Fellbaum","year":"1998","unstructured":"Fellbaum C (1998) WordNet: an electronic database. MIT Press, Cambridge, MA"},{"key":"993_CR20","unstructured":"Strapparava C, Valitutti A (2004) WordNet-affect: an affective extension of WordNet. In: Proceedings of the 4th international conference on language resources and evaluation (LREC 2004), pp 1083\u20131086"},{"key":"993_CR21","first-page":"200","volume-title":"Language resources, linguistic theory","author":"S Cerini","year":"2007","unstructured":"Cerini S, Compagnoni V, Demontis A, Formentelli M, Gandini C (2007) Micro-WNOp: a gold standard for the evaluation of automatically compiled lexical resources for opinion mining. In: Sanso A (ed) Language resources, linguistic theory. Franco Angeli, Milan, pp 200\u2013210"},{"key":"993_CR22","doi-asserted-by":"publisher","unstructured":"Stone PJ, Hunt EB (1963) A computer approach to content analysis: studies using the general inquirer system. In: Proceedings of the spring joint computer conference (AFIPS 1963), pp 241\u2013256","DOI":"10.1145\/1461551.1461583"},{"key":"993_CR23","unstructured":"de Albornoz JC, Plaza L, Gervas P (2012) Sentisense: an easily scalable concept based affective lexicon for sentiment analysis. In: Proceedings of the 8th international conference on language resources and evaluation (LREC 2012), pp 3562\u20133567"},{"key":"993_CR24","unstructured":"Nielsen FA (2011) A new ANEW: evaluation of a word list for sentiment analysis in microblogs, CoRR abs\/1103.2903"},{"issue":"2","key":"993_CR25","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1162\/COLI_a_00049","volume":"37","author":"M Taboada","year":"2011","unstructured":"Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267\u2013307","journal-title":"Comput Linguist"},{"key":"993_CR26","doi-asserted-by":"publisher","unstructured":"Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2004), pp 168\u2013177","DOI":"10.1145\/1014052.1014073"},{"key":"993_CR27","doi-asserted-by":"publisher","unstructured":"Riloff E, Wiebe J (2003) Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 conference on empirical methods in natural language processing (EMNLP 2003), pp 105\u2013112","DOI":"10.3115\/1119355.1119369"},{"key":"993_CR28","unstructured":"Cambria E, Havasi C, Hussain A (2012) Senticnet 2: a semantic and affective resource for opinion mining and sentiment analysis. In: Proceedings of the 25th Florida artificial intelligence research society conference (FLAIRS 2012), pp 202\u2013207"},{"key":"993_CR29","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, vol 1. Association for Computational Linguistics, pp 142\u2013150"},{"key":"993_CR30","doi-asserted-by":"publisher","unstructured":"Pang B, Lee L (2005) Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd annual meeting on association for computational linguistics, pp 115\u2013124","DOI":"10.3115\/1219840.1219855"},{"key":"993_CR31","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"},{"key":"993_CR32","doi-asserted-by":"publisher","unstructured":"Khan FH, Qamar U, Bashir S (2016) Multi-objective model selection (MOMS)-based semi-supervised framework for sentiment analysis. Cognit Comput. doi: 10.1007\/s12559-016-9386-8","DOI":"10.1007\/s12559-016-9386-8"},{"key":"993_CR33","unstructured":"Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: International conference on language resources and evaluation (LREC), vol 10, pp 2200\u20132204"},{"key":"993_CR34","volume-title":"Machine learning","author":"T Mitchell","year":"1996","unstructured":"Mitchell T (1996) Machine learning. McCraw Hill, New YorK"},{"key":"993_CR35","unstructured":"Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: ICML, vol 97, pp 412\u2013420"},{"key":"993_CR36","unstructured":"Lewis DD, Ringuette M (1994) Comparison of two learning algorithms for text categorization. In: Proceedings of third annual symposium on document analysis and information retrieval"},{"issue":"3","key":"993_CR37","doi-asserted-by":"publisher","first-page":"491","DOI":"10.13053\/cys-18-3-2043","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. Computaci\u00f3n y Sistemas 18(3):491\u2013504","journal-title":"Computaci\u00f3n y Sistemas"},{"key":"993_CR38","doi-asserted-by":"publisher","unstructured":"Basu T, Murthy CA (2012) Effective text classification by a supervised feature selection approach. In: Data mining workshops (ICDMW), 2012 IEEE 12th international conference on IEEE, pp 918\u2013925","DOI":"10.1109\/ICDMW.2012.45"},{"issue":"2","key":"993_CR39","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":"993_CR40","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2011","unstructured":"Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques. Elsevier, Amsterdam"},{"key":"993_CR41","unstructured":"Verma S, Bhattacharyya P (2009) Incorporating semantic knowledge for sentiment analysis. In: 6th international conference on natural language processing India"},{"key":"993_CR42","doi-asserted-by":"publisher","unstructured":"Kalaivani P, Shunmuganathan KL (2015) Feature reduction based on genetic algorithm and hybrid model for opinion mining. Sci Program. doi: 10.1155\/2015\/961454","DOI":"10.1155\/2015\/961454"},{"issue":"6","key":"993_CR43","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1016\/j.ins.2010.11.023","volume":"181","author":"R Xia","year":"2011","unstructured":"Xia R, Zong C, Li S (2011) Ensemble of feature sets and classification algorithms for sentiment classification. Inf Sci 181(6):1138\u20131152","journal-title":"Inf Sci"},{"key":"993_CR44","unstructured":"Varela PL, Martins AF, Aguiar PM, Figueiredo MA (2013) An empirical study of feature selection for sentiment analysis. In: 9th conference on telecommunications, Conftele, Castelo Branco"},{"key":"993_CR45","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/MIS.2013.1","volume":"2","author":"C Hung","year":"2013","unstructured":"Hung C, Lin HK (2013) Using objective words in SentiWordNet to improve word-of-mouth sentiment classification. IEEE Intell Syst 2:47\u201354","journal-title":"IEEE Intell Syst"},{"key":"993_CR46","unstructured":"Rice DR, Zorn C (2013) Corpus-based dictionaries for sentiment analysis of specialized vocabularies. In: Proceedings of NDATAD"},{"key":"993_CR47","doi-asserted-by":"publisher","unstructured":"Demiroz G, Yanikoglu B, Tapucu D, Saygin Y (2012) Learning domain-specific polarity lexicons. In: Data mining workshops (ICDMW). In: 2012 IEEE 12th international conference on IEEE, pp 674\u2013679","DOI":"10.1109\/ICDMW.2012.120"},{"key":"993_CR48","unstructured":"Sharma A, Dey S (2012) Performance investigation of feature selection methods and sentiment lexicons for sentiment analysis. In: Special issue of international journal of computer applications (0975 \u2013 8887) on advanced computing and communication technologies for HPC Applications \u2013 ACCTHPCA"},{"key":"993_CR49","doi-asserted-by":"publisher","unstructured":"Mudinas A, Zhang D, Levene M (2012) Combining lexicon and learning based approaches for concept-level sentiment analysis. In: Proceedings of the first international workshop on issues of sentiment discovery and opinion mining. ACM, p 5","DOI":"10.1145\/2346676.2346681"},{"issue":"4","key":"993_CR50","first-page":"199","volume":"2","author":"A Hamouda","year":"2011","unstructured":"Hamouda A, Marei M, Rohaim M (2011) Building machine learning based senti-word lexicon for sentiment analysis. J Adv Inf Technol 2(4):199\u2013203","journal-title":"J Adv Inf Technol"},{"key":"993_CR51","doi-asserted-by":"publisher","unstructured":"Su F, Markert K (2008) From words to senses: a case study of subjectivity recognition. In: Proceedings of the 22nd international conference on computational linguistics, vol 1. Association for Computational Linguistics, pp 825\u2013832","DOI":"10.3115\/1599081.1599185"},{"key":"993_CR52","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1155\/2015\/715730","volume":"9","author":"B Agarwal","year":"2015","unstructured":"Agarwal B, Mittal N, Bansal P, Garg S (2015) Sentiment analysis using common-sense and context information. Comput Intell Neurosci 9:715\u2013730. doi: 10.1155\/2015\/715730","journal-title":"Comput Intell Neurosci"},{"key":"993_CR53","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.dss.2013.08.002","volume":"57","author":"G Wang","year":"2014","unstructured":"Wang G, Sun J, Ma J, Xu K, Gu J (2014) Sentiment classification: the contribution of ensemble learning. Decision Support Syst 57:77\u201393","journal-title":"Decision Support Syst"},{"key":"993_CR54","unstructured":"Dhande LL, Patnaik GK (2014) Analyzing sentiment of movie review data using naive bayes neural classifier. Int J Emerg Trends Technol Comput Sci (IJETTCS)"},{"key":"993_CR55","unstructured":"Zhou S, Chen Q, Wang X, Li X (2014) Hybrid deep belief networks for semi-supervised sentiment classification. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics. Technical Papers, pp 1341\u20131349"},{"key":"993_CR56","doi-asserted-by":"crossref","unstructured":"Liu B, Blasch E, Chen Y, Shen D, Chen G (2013) Scalable sentiment classification for big data analysis using naive bayes classifier. In: Big data, 2013 IEEE international conference on IEEE, pp 99\u2013104","DOI":"10.1109\/BigData.2013.6691740"},{"key":"993_CR57","unstructured":"Socher R, Pennington J, Huang EH, Ng AY, Manning CD (2011) Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the conference on empirical methods in natural language processing. pp 151\u2013161"},{"issue":"4","key":"993_CR58","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.ipm.2010.11.003","volume":"47","author":"Y He","year":"2011","unstructured":"He Y, Zhou D (2011) Self-training from labeled features for sentiment analysis. Inf Process Manag 47(4):606\u2013616","journal-title":"Inf Process Manag"},{"key":"993_CR59","unstructured":"Lin C, He Y, Everson Y (2010) A comparative study of Bayesian models for unsupervised sentiment. In: Proceedings of the fourteenth conference on computational natural language learning. Uppsala, Sweden, pp 144\u2013152"},{"key":"993_CR60","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.patrec.2015.01.004","volume":"56","author":"S Park","year":"2015","unstructured":"Park S, Lee W, Moon IC (2015) Efficient extraction of domain specific sentiment lexicon with active learning. Pattern Recognit Lett 56:38\u201344","journal-title":"Pattern Recognit Lett"},{"key":"993_CR61","unstructured":"Agarwal B, Mittal N (2013) Sentiment classification using rough set based hybrid feature selection. In: Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis (WASSA), 2013, June, pp 115\u2013119"},{"issue":"4","key":"993_CR62","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1109\/MIS.2009.105","volume":"25","author":"Y Dang","year":"2010","unstructured":"Dang Y, Zhang Y, Chen H (2010) A lexicon-enhanced method for sentiment classification: an experiment on online product reviews. IEEE Intell Syst 25(4):46\u201353","journal-title":"IEEE Intell Syst"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-016-0993-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-016-0993-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-016-0993-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T19:38:33Z","timestamp":1568403513000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-016-0993-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,20]]},"references-count":62,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2017,6]]}},"alternative-id":["993"],"URL":"https:\/\/doi.org\/10.1007\/s10115-016-0993-1","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,9,20]]}}}