Predictive Features in Semi-Supervised Learning for Polarity Classification and the Role of Adjectives
- 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.
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| Author: | Michael WiegandGND, Dietrich Klakow |
|---|---|
| URN: | urn:nbn:de:bsz:mh39-84588 |
| Handle: | http://hdl.handle.net/10062/9763 |
| ISSN: | 1736-6305 |
| Parent Title (English): | Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009), May 14-16, 2009, Odense, Denmark |
| Series (Serial Number): | NEALT Proceedings Series (4) |
| Publisher: | Northern European Association for Language Technology |
| Place of publication: | Uppsala |
| Editor: | Kristiina Jokinen, Eckhard Bick |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2009 |
| Date of Publication (online): | 2019/01/30 |
| Publicationstate: | Veröffentlichungsversion |
| Reviewstate: | Peer-Review |
| Tag: | Sentimentanalyse |
| GND Keyword: | Computerlinguistik; Maschinelles Lernen; Natürliche Sprache; Polarität; Text Mining |
| First Page: | 198 |
| Last Page: | 205 |
| DDC classes: | 400 Sprache / 400 Sprache, Linguistik |
| Open Access?: | ja |
| Linguistics-Classification: | Computerlinguistik |
| Licence (German): |  Urheberrechtlich geschützt |