Lack of training data in sentiment classification: current solution
In recent years, sentiment classification has attracted much attention from natural language processing researchers. Most of researchers in this field consider sentiment classification as a supervised classification problem and train a classifier from a large number of labelled documents. . Unfortun...
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| Main Authors: | , |
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| Format: | Article |
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Suryansh Publications
2012
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/31074/ http://www.ijrcct.org/index.php/ojs/article/view/51 |
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| Summary: | In recent years, sentiment classification has attracted much attention from natural language processing researchers. Most of researchers in this field consider sentiment classification as a supervised classification problem and train a classifier from a large number of labelled documents. . Unfortunately, in some language other than English, a reliable and sufficient labelled data is not always available and manually labelling a reliable and rich training data is very time-consuming. Until now, researchers have developed several techniques to the solution of the problem. This paper try to cover some techniques and approaches that be used in this area. |
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