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Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples

In recent years, research in sentiment classification has received considerable attention by natural language processing researchers. Annotated sentiment corpora are the most important resources used in sentiment classification. However, since most recent research works in this field have focused on...

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Main Authors: Hajmohammadi, Mohammad Sadegh, Ibrahim, Roliana, Selamat, Ali, Fujita, Hamido
Format: Article
Published: Elsevier Inc. 2015
Subjects:
QA76 Computer software
Online Access:http://eprints.utm.my/id/eprint/58077/
http://dx.doi.org/10.1016/j.ins.2015.04.003
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spelling my.utm.580772022-04-05T04:57:46Z http://eprints.utm.my/id/eprint/58077/ Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples Hajmohammadi, Mohammad Sadegh Ibrahim, Roliana Selamat, Ali Fujita, Hamido QA76 Computer software In recent years, research in sentiment classification has received considerable attention by natural language processing researchers. Annotated sentiment corpora are the most important resources used in sentiment classification. However, since most recent research works in this field have focused on the English language, there are accordingly not enough annotated sentiment resources in other languages. Manual construction of reliable annotated sentiment corpora for a new language is a labour-intensive and time-consuming task. Projection of sentiment corpus from one language into another language is a natural solution used in cross-lingual sentiment classification. Automatic machine translation services are the most commonly tools used to directly project information from one language into another. However, since term distribution across languages may be different due to variations in linguistic terms and writing styles, cross-lingual methods cannot reach the performance of monolingual methods. In this paper, a novel learning model is proposed based on the combination of uncertainty-based active learning and semi-supervised self-training approaches to incorporate unlabelled sentiment documents from the target language in order to improve the performance of cross-lingual methods. Further, in this model, the density measures of unlabelled examples are considered in active learning part in order to avoid outlier selection. The empirical evaluation on book review datasets in three different languages shows that the proposed model can significantly improve the performance of cross-lingual sentiment classification in comparison with other existing and baseline methods. Elsevier Inc. 2015 Article PeerReviewed Hajmohammadi, Mohammad Sadegh and Ibrahim, Roliana and Selamat, Ali and Fujita, Hamido (2015) Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples. Information Sciences, 317 . pp. 67-77. ISSN 0020-0255 http://dx.doi.org/10.1016/j.ins.2015.04.003
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 QA76 Computer software
spellingShingle QA76 Computer software
Hajmohammadi, Mohammad Sadegh
Ibrahim, Roliana
Selamat, Ali
Fujita, Hamido
Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples
description In recent years, research in sentiment classification has received considerable attention by natural language processing researchers. Annotated sentiment corpora are the most important resources used in sentiment classification. However, since most recent research works in this field have focused on the English language, there are accordingly not enough annotated sentiment resources in other languages. Manual construction of reliable annotated sentiment corpora for a new language is a labour-intensive and time-consuming task. Projection of sentiment corpus from one language into another language is a natural solution used in cross-lingual sentiment classification. Automatic machine translation services are the most commonly tools used to directly project information from one language into another. However, since term distribution across languages may be different due to variations in linguistic terms and writing styles, cross-lingual methods cannot reach the performance of monolingual methods. In this paper, a novel learning model is proposed based on the combination of uncertainty-based active learning and semi-supervised self-training approaches to incorporate unlabelled sentiment documents from the target language in order to improve the performance of cross-lingual methods. Further, in this model, the density measures of unlabelled examples are considered in active learning part in order to avoid outlier selection. The empirical evaluation on book review datasets in three different languages shows that the proposed model can significantly improve the performance of cross-lingual sentiment classification in comparison with other existing and baseline methods.
format Article
author Hajmohammadi, Mohammad Sadegh
Ibrahim, Roliana
Selamat, Ali
Fujita, Hamido
author_facet Hajmohammadi, Mohammad Sadegh
Ibrahim, Roliana
Selamat, Ali
Fujita, Hamido
author_sort Hajmohammadi, Mohammad Sadegh
title Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples
title_short Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples
title_full Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples
title_fullStr Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples
title_full_unstemmed Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples
title_sort combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples
publisher Elsevier Inc.
publishDate 2015
url http://eprints.utm.my/id/eprint/58077/
http://dx.doi.org/10.1016/j.ins.2015.04.003
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score 13.211869

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