{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,13]],"date-time":"2026-11-13T03:44:26Z","timestamp":1794541466997,"version":"4.9.1"},"publisher-location":"Boston, MA","reference-count":26,"publisher":"Springer US","isbn-type":[{"value":"9781489976857","type":"print"},{"value":"9781489976871","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-1-4899-7687-1_905","type":"book-chapter","created":{"date-parts":[[2017,4,13]],"date-time":"2017-04-13T08:33:02Z","timestamp":1492072382000},"page":"938-947","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Opinion Stream Mining"],"prefix":"10.1007","author":[{"given":"Myra","family":"Spiliopoulou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eirini","family":"Ntoutsi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Zimmermann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,4,14]]},"reference":[{"key":"905_CR1560","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1137\/1.9781611972764.44","volume-title":"Proceedings of 6th SIAM international conference on data mining (SDM\u201906)","author":"CC Aggarwal","year":"2006","unstructured":"Aggarwal CC, Yu PS (2006) A framework for clustering massive text and categorical data. In: Proceedings of 6th SIAM international conference on data mining (SDM\u201906), Bethesda. SIAM, pp\u00a0479\u2013483"},{"key":"905_CR1561","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1201\/b17320","volume-title":"Data classification: algorithms and applications, chapter 11","author":"C Aggarwal","year":"2014","unstructured":"Aggarwal C, Zhai C (2014) Text classification. In: Aggarwal C (ed) Data classification: algorithms and applications, chapter 11. Chapman & Hall\/CRC, Boca Raton, pp\u00a0287\u2013336"},{"key":"905_CR1562","first-page":"373","volume-title":"On-line LDA: adaptive topic models for mining text streams with applications to topic detection and tracking","author":"L AlSumait","year":"2008","unstructured":"AlSumait L, Barbara D, Domeniconi C (2008) On-line LDA: adaptive topic models for mining text streams with applications to topic detection and tracking. In: Proceedings of 2008 IEEE conference on data mining (ICDM\u201908), Pisa. IEEE, pp\u00a0373\u2013382"},{"key":"905_CR1563","first-page":"1","volume-title":"Proceedings of the 13th international conference on discovery science (DS\u201910)","author":"A Bifet","year":"2010","unstructured":"Bifet A, Frank E (2010) Sentiment knowledge discovery in Twitter streaming data. In: Proceedings of the 13th international conference on discovery science (DS\u201910), Canberra. Springer, pp\u00a01\u201315"},{"key":"905_CR1564","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-642-03915-7_22","volume-title":"Proceedings of the 8th international symposium on intelligent data analysis: advances in intelligent data analysis VIII (IDA)","author":"A Bifet","year":"2009","unstructured":"Bifet A, Gavald\u00e0 R (2009) Adaptive learning from evolving data streams. In: Proceedings of the 8th international symposium on intelligent data analysis: advances in intelligent data analysis VIII (IDA), Lyon. Springer, pp\u00a0249\u2013260"},{"key":"905_CR1565","doi-asserted-by":"crossref","unstructured":"Blei DM, Lafferty JD (2006) Dynamic topic models. In: Proceedings of 23rd international conference on machine learning (ICML\u201906), Pittsburgh, pp\u00a0113\u2013120","DOI":"10.1145\/1143844.1143859"},{"key":"905_CR1566","first-page":"92","volume-title":"Proceedings of 11th conference on computational learning theory","author":"A Blum","year":"1998","unstructured":"Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of 11th conference on computational learning theory, Madison. ACM, pp\u00a092\u2013100"},{"key":"905_CR1567","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262033589.001.0001","volume-title":"Semi-supervised learning","author":"O Chapelle","year":"2006","unstructured":"Chapelle O, Sch\u00f6lkopf B, Zien A (2006) Semi-supervised learning. MIT, Cambridge"},{"key":"905_CR1568","doi-asserted-by":"crossref","unstructured":"Cheng Y, Chen Z, Liu L, Wang J, Agrawal A, Choudhary A (2013) Feedback-driven multiclass active learning for data streams. In: Proceedings of 22nd international conference on information and knowledge management (CIKM\u201913), San Fransisco, pp\u00a01311\u20131320","DOI":"10.1145\/2505515.2505528"},{"issue":"C","key":"905_CR1569","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.knosys.2015.02.021","volume":"82","author":"X Fu","year":"2015","unstructured":"Fu X, Yang K, Huang JZ, Cui L (2015) Dynamic non-parametric joint sentiment topic mixture model. Know-Based Syst 82(C):102\u2013114","journal-title":"Know-Based Syst"},{"key":"905_CR1570","doi-asserted-by":"crossref","unstructured":"Gama J, \u017dliobait\u0117 I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv 46(4):44:1\u201344:37","DOI":"10.1145\/2523813"},{"key":"905_CR1571","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.neucom.2012.08.020","volume":"101","author":"H Gan","year":"2013","unstructured":"Gan H, Sang N, Huang R, Tong X, Dan Z (2013) Using clustering analysis to improve semi-supervised classification. Neurocomputing 101:290\u2013298","journal-title":"Neurocomputing"},{"key":"905_CR1572","doi-asserted-by":"crossref","unstructured":"Gohr A, Hinneburg A, Schult R, Spiliopoulou M (2009) Topic evolution in a stream of documents. In: SIAM data mining conference (SDM\u201909), Reno, pp\u00a0378\u2013385","DOI":"10.1137\/1.9781611972795.74"},{"key":"905_CR1573","doi-asserted-by":"crossref","unstructured":"Gokulakrishnan B, Priyanthan P, Ragavan T, Prasath N, Perera A (2012) Opinion mining and sentiment analysis on a Twitter data stream. In: Proceedings of the 2012 international conference on advances in ICT for emerging regions (ICTer), Colombo, pp\u00a0182\u2013188","DOI":"10.1109\/ICTer.2012.6423033"},{"issue":"2","key":"905_CR1574","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.ipm.2014.04.001","volume":"51","author":"J Kranjc","year":"2015","unstructured":"Kranjc J, Smailovic J, Podpecan V, Grcar M, Znidarsic M, Lavrac N (2015) Active learning for sentiment analysis on data streams: methodology and workflow implementation in the ClowdFlows platform. Inf Process Manag 51(2):187\u2013203","journal-title":"Inf Process Manag"},{"issue":"1","key":"905_CR1575","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00416ED1V01Y201204HLT016","volume":"5","author":"B Liu","year":"2012","unstructured":"Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1\u2013167","journal-title":"Synth Lect Hum Lang Technol"},{"key":"905_CR1576","doi-asserted-by":"crossref","unstructured":"Liu S, Li F, Li F, Cheng X, Shen H (2013) Adaptive co-training SVM for sentiment classification on tweets. In: Proceedings of 22nd international conference on information and knowledge management (CIKM\u201913), San Fransisco, pp\u00a02079\u20132088","DOI":"10.1145\/2505515.2505569"},{"key":"905_CR1577","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1145\/2507157.2507163","volume-title":"Proceedings of 7th ACM conference on recommender systems (RecSys\u201913)","author":"J McAuley","year":"2013","unstructured":"McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of 7th ACM conference on recommender systems (RecSys\u201913), Hong Kong. ACM, pp\u00a0165\u2013172"},{"key":"905_CR1578","unstructured":"Saveski M, Grcar M (2011) Web services for stream mining: a stream-based active learning use case. In: Proceedings of the workshop \u201cPlanning to Learn and Service-Oriented Knowledge Discovery\u201d at ECML PKDD 2011, Athens"},{"key":"905_CR1579","doi-asserted-by":"crossref","unstructured":"Wagner S, Zimmermann M, Ntoutsi E, Spiliopoulou M (2015) Ageing-based multinomial naive bayes classifiers over opinionated data streams. In: European conference on machine learning and principles and practice of knowledge discovery in databases (ECMLPKDD\u201915), Porto, 07\u201311 Sept 2015. Volume 9284 of lecture notes in computer science. Springer International Publishing","DOI":"10.1007\/978-3-319-23528-8_25"},{"key":"905_CR1580","doi-asserted-by":"crossref","unstructured":"Wang X, McCallum A (2006) Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of 12th ACM SIGKDD international conference on knowledge discovery and data mining (KDD\u201906), Philadelphia, pp\u00a0424\u2013433","DOI":"10.1145\/1150402.1150450"},{"key":"905_CR1581","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1016\/j.neucom.2013.04.017","volume":"120","author":"S Zhou","year":"2013","unstructured":"Zhou S, Chen Q, Wang X (2013) Active deep learning method for semi-supervised sentiment classification. Neurocomputing 120:536\u2013546","journal-title":"Neurocomputing"},{"key":"905_CR1582","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.neucom.2014.04.079","volume":"150","author":"M Zimmermann","year":"2015","unstructured":"Zimmermann M, Ntoutsi E, Spiliopoulou M (2015a) Discovering and monitoring product features and the opinions on them with OPINSTREAM. Neurocomputing 150:318\u2013330","journal-title":"Neurocomputing"},{"key":"905_CR1583","unstructured":"Zimmermann M, Ntoutsi E, Spiliopoulou M (2015b) Incremental active opinion learning over a stream of opinionated documents. In: WISDOM\u201915 (workshop on issues of sentiment discovery and opinion mining) at KDD\u201915, Sydney"},{"key":"905_CR1584","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1016\/j.ins.2015.06.050","volume":"329","author":"M Zimmermann","year":"2016","unstructured":"Zimmermann M, Ntoutsi E, Spiliopoulou M (2016) Extracting opinionated (sub)features from a stream of product reviews using accumulated novelty and internal re-organization. Inf Sci 329:876\u2013899","journal-title":"Inf Sci"},{"key":"905_CR1585","doi-asserted-by":"crossref","unstructured":"Zliobaite I, Bifet A, Pfahringer B, Holmes G (2011) Active learning with evolving streaming data. In: Proceedings of ECML PKDD 2011, Athens. Volume 6913 of LNCS. Springer","DOI":"10.1007\/978-3-642-23808-6_39"}],"container-title":["Encyclopedia of Machine Learning and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-1-4899-7687-1_905","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T16:07:38Z","timestamp":1791821258000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-1-4899-7687-1_905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9781489976857","9781489976871"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-1-4899-7687-1_905","relation":{"is-part-of":[{"id-type":"doi","id":"https:\/\/doi.org\/10.1007\/978-1-4899-7687-1","asserted-by":"object"}]},"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"14 April 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}