Partially Supervised Phrase-Level Sentiment Classification
SCIE
SCOPUS
- Title
- Partially Supervised Phrase-Level Sentiment Classification
- Authors
- Nam S.-H; Na S.-H; Kim J; Lee Y; Lee J.-H.
- Date Issued
- 2009-03
- Publisher
- Springer
- Abstract
- This paper presents a new partially supervised approach to phrase-level sentiment analysis that first automatically constructs a polarity-tagged corpus and then learns sequential sentiment tag from the corpus. This approach uses only sentiment sentences which are readily available on the Internet and does not use a polarity-tagged corpus which is hard to construct manually. With this approach, the system is able to automatically classify phrase-level sentiment. The result shows that a system can learn sentiment expressions without a polarity-tagged corpus.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/35950
- DOI
- 10.1007/978-3-642-00831-3_21
- ISSN
- 0302-9743
- Article Type
- Article
- Citation
- LECTURE NOTES IN COMPUTER SCIENCE, vol. 5459/2009, page. 225 - 235, 2009-03
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