@inproceedings{WiegandKlakow2019, author = {Michael Wiegand and Dietrich Klakow}, title = {Predictive Features in Semi-Supervised Learning for Polarity Classification and the Role of Adjectives}, series = {Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009), May 14-16, 2009, Odense, Denmark}, editor = {Kristiina Jokinen and Eckhard Bick}, publisher = {Northern European Association for Language Technology}, address = {Uppsala}, issn = {1736-6305}, url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84588}, pages = {198 -- 205}, year = {2019}, abstract = {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.}, language = {en} }