Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classif...

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Main Authors:	Al-Saffar, Ahmed Ali Mohammed, Suryanti, Awang, Tao, Hai, Nazlia, Omar, Al-Saiagh, Wafaa, Al-bared, Mohammed
Format:	Article
Language:	English
Published:	Public Library of Science 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/21130/ http://umpir.ump.edu.my/id/eprint/21130/ http://umpir.ump.edu.my/id/eprint/21130/ http://umpir.ump.edu.my/id/eprint/21130/1/Malay%20sentiment%20analysis%20based%20on%20combined%20classification.pdf

id	ump-21130
recordtype	eprints
spelling	ump-211302018-05-04T07:38:54Z http://umpir.ump.edu.my/id/eprint/21130/ Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm Al-Saffar, Ahmed Ali Mohammed Suryanti, Awang Tao, Hai Nazlia, Omar Al-Saiagh, Wafaa Al-bared, Mohammed QA75 Electronic computers. Computer science Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach. Public Library of Science 2018 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/21130/1/Malay%20sentiment%20analysis%20based%20on%20combined%20classification.pdf Al-Saffar, Ahmed Ali Mohammed and Suryanti, Awang and Tao, Hai and Nazlia, Omar and Al-Saiagh, Wafaa and Al-bared, Mohammed (2018) Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm. PLoS ONE, 13 (4). pp. 1-18. ISSN 1932-6203 https://doi.org/10.1371/journal.pone.0194852 doi: 10.1371/journal.pone.0194852
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Al-Saffar, Ahmed Ali Mohammed Suryanti, Awang Tao, Hai Nazlia, Omar Al-Saiagh, Wafaa Al-bared, Mohammed Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm
description	Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach.
format	Article
author	Al-Saffar, Ahmed Ali Mohammed Suryanti, Awang Tao, Hai Nazlia, Omar Al-Saiagh, Wafaa Al-bared, Mohammed
author_facet	Al-Saffar, Ahmed Ali Mohammed Suryanti, Awang Tao, Hai Nazlia, Omar Al-Saiagh, Wafaa Al-bared, Mohammed
author_sort	Al-Saffar, Ahmed Ali Mohammed
title	Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm
title_short	Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm
title_full	Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm
title_fullStr	Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm
title_full_unstemmed	Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm
title_sort	malay sentiment analysis based on combined classification approaches and senti-lexicon algorithm
publisher	Public Library of Science
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/21130/ http://umpir.ump.edu.my/id/eprint/21130/ http://umpir.ump.edu.my/id/eprint/21130/ http://umpir.ump.edu.my/id/eprint/21130/1/Malay%20sentiment%20analysis%20based%20on%20combined%20classification.pdf
first_indexed	2023-09-18T22:30:53Z
last_indexed	2023-09-18T22:30:53Z
_version_	1777416277532344320

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm

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