TY - CHAP U1 - Konferenzveröffentlichung A1 - Wiegand, Michael A1 - Klakow, Dietrich ED - Jokinen, Kristiina ED - Bick, Eckhard T1 - Predictive Features in Semi-Supervised Learning for Polarity Classification and the Role of Adjectives T2 - Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009), May 14-16, 2009, Odense, Denmark N2 - In opinion mining, there has been only very little work investigating semi-supervised machine learning on document-level polarity classification. We show that semi-supervised learning performs significantly better than supervised learning when only few labelled data are available. Semi-supervised polarity classifiers rely on a predictive feature set. (Semi-)Manually built polarity lexicons are one option but they are expensive to obtain and do not necessarily work in an unknown domain. We show that extracting frequently occurring adjectives & adverbs of an unlabeled set of in-domain documents is an inexpensive alternative which works equally well throughout different domains. T3 - NEALT Proceedings Series - 4 KW - Computerlinguistik KW - Text Mining KW - Natürliche Sprache KW - Maschinelles Lernen KW - Polarität KW - Sentimentanalyse Y1 - 2009 U6 - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84588 UN - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84588 SN - 1736-6305 SS - 1736-6305 SP - 198 EP - 205 PB - Northern European Association for Language Technology CY - Uppsala ER -