The Role of Knowledge-based Features in Polarity Classification at Sentence Level
- Though polarity classification has been extensively explored at document level, there has been little work investigating feature design at sentence level. Due to the small number of words within a sentence, polarity classification at sentence level differs substantially from document-level classification in that resulting bag-of-words feature vectors tend to be very sparse resulting in a lower classification accuracy. In this paper, we show that performance can be improved by adding features specifically designed for sentence-level polarity classification. We consider both explicit polarity information and various linguistic features. A great proportion of the improvement that can be obtained by using polarity information can also be achieved by using a set of simple domain-independent linguistic features.
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| Author: | Michael WiegandGND, Dietrich Klakow |
|---|---|
| URN: | urn:nbn:de:bsz:mh39-84390 |
| URL: | https://www.aaai.org/ocs/index.php/FLAIRS/2009/paper/view/24 |
| ISBN: | 978-1-57735-419-2 |
| Parent Title (English): | Proceedings of the Twenty-Second International Florida Artificial Intelligence Research Society Conference, 19–21 May 2009, Sanibel Island, Florida, USA |
| Publisher: | AAAI Press |
| Place of publication: | Menlo Park, CA |
| Editor: | H. Chad LaneGND, Hans W. GuesgenGND |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2009 |
| Date of Publication (online): | 2019/01/23 |
| Publicationstate: | Veröffentlichungsversion |
| Reviewstate: | Peer-Review |
| Tag: | Sentimentanalyse |
| GND Keyword: | Computerlinguistik; Natürliche Sprache; Polarität; Text Mining |
| First Page: | 296 |
| Last Page: | 301 |
| DDC classes: | 400 Sprache / 400 Sprache, Linguistik |
| Open Access?: | ja |
| Linguistics-Classification: | Computerlinguistik |
| Licence (German): |  Urheberrechtlich geschützt |