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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: Hajmohammadi, Mohammad Sadegh, Ibrahim, Roliana
Format: Article
Published: Suryansh Publications 2012
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/31074/
http://www.ijrcct.org/index.php/ojs/article/view/51
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spelling 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
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Hajmohammadi, Mohammad Sadegh
Ibrahim, Roliana
Lack of training data in sentiment classification: current solution
description 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.
format Article
author Hajmohammadi, Mohammad Sadegh
Ibrahim, Roliana
author_facet Hajmohammadi, Mohammad Sadegh
Ibrahim, Roliana
author_sort 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
title_fullStr Lack of training data in sentiment classification: current solution
title_full_unstemmed Lack of training data in sentiment classification: current solution
title_sort lack of training data in sentiment classification: current solution
publisher Suryansh Publications
publishDate 2012
url http://eprints.utm.my/id/eprint/31074/
http://www.ijrcct.org/index.php/ojs/article/view/51
_version_ 1643648659590479872
score 13.18916

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