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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my.utm.310742018-11-30T07:09:37Z http://eprints.utm.my/id/eprint/31074/ Lack of training data in sentiment classification: current solution Hajmohammadi, Mohammad Sadegh Ibrahim, Roliana QA75 Electronic computers. Computer science 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. Suryansh Publications 2012-09 Article PeerReviewed Hajmohammadi, Mohammad Sadegh and Ibrahim, Roliana (2012) Lack of training data in sentiment classification: current solution. International Journal of Research in Computer and Communication Technology, 1 (4). pp. 133-138. ISSN 2278-5841 http://www.ijrcct.org/index.php/ojs/article/view/51 |
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QA75 Electronic computers. Computer science Hajmohammadi, Mohammad Sadegh Ibrahim, Roliana Lack of training data in sentiment classification: current solution |
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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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Article |
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Hajmohammadi, Mohammad Sadegh Ibrahim, Roliana |
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Hajmohammadi, Mohammad Sadegh Ibrahim, Roliana |
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Hajmohammadi, Mohammad Sadegh |
title |
Lack of training data in sentiment classification: current solution |
title_short |
Lack of training data in sentiment classification: current solution |
title_full |
Lack of training data in sentiment classification: current solution |
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Lack of training data in sentiment classification: current solution |
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Lack of training data in sentiment classification: current solution |
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lack of training data in sentiment classification: current solution |
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Suryansh Publications |
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2012 |
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http://eprints.utm.my/id/eprint/31074/ http://www.ijrcct.org/index.php/ojs/article/view/51 |
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