Konferenzbeitrag
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.
- Language
-
Englisch
- Subject
-
Computerlinguistik
Text Mining
Natürliche Sprache
Maschinelles Lernen
Polarität
Sprache
- Event
-
Geistige Schöpfung
- (who)
-
Wiegand, Michael
Klakow, Dietrich
- Event
-
Veröffentlichung
- (who)
-
Uppsala : Northern European Association for Language Technology
- (when)
-
2019-01-30
- URN
-
urn:nbn:de:bsz:mh39-84588
- Last update
-
06.03.2025, 9:00 AM CET
Data provider
This object is provided by:
Leibniz-Institut für Deutsche Sprache - Bibliothek. If you have any questions about the object, please contact the data provider.
Leibniz-Institut für Deutsche Sprache - Bibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Konferenzbeitrag
Associated
- Wiegand, Michael
- Klakow, Dietrich
- Uppsala : Northern European Association for Language Technology
Time of origin
- 2019-01-30
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