Improving sentiment analysis in Arabic: A combined approach

Sentiment analysis SA is concerned with determining users’ opinions from text reviews. However, mining these opinions from massive amounts of unstructured and lengthy documents is a challenging task. In this paper, we propose methods that extract valuable opinions and values from online movie review...

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Autores principales:	Belgacem Brahimi, Mohamed Touahria, Abdelkamel Tari
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Text mining Opinion mining Sentiment classification Review extraction Combined approach Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/0433758789d94bee92da2dbf51a12e3d
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spelling	oai:doaj.org-article:0433758789d94bee92da2dbf51a12e3d2021-11-22T04:19:42ZImproving sentiment analysis in Arabic: A combined approach1319-157810.1016/j.jksuci.2019.07.011https://doaj.org/article/0433758789d94bee92da2dbf51a12e3d2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1319157819303283https://doaj.org/toc/1319-1578Sentiment analysis SA is concerned with determining users’ opinions from text reviews. However, mining these opinions from massive amounts of unstructured and lengthy documents is a challenging task. In this paper, we propose methods that extract valuable opinions and values from online movie reviews to improve SA in Arabic. First, we propose a method that explores the role of n-gram and skip-n-gram models in opinion classification. Second, we study a method that exploits subjective words such as adjectives and nouns by applying Part-Of Speech tagging. Both of the methods are combined with a feature reduction technique to enhance SA results. Third, we present a method that seeks to extract relevant opinions such as review summaries and conclusion opinions. Then, a combined approach is proposed to augment opinion classification results. Forth, we introduce a method for analyzing customers’ opinions by determining factors impacting their attitudes based on the costumer value model. Experimental results conducted on two datasets prove that our proposed methods are effective and provide better scores than baseline sentiment classifiers. The best obtained classification results reached 96% in F-Measure. These results indicate also that the aesthetic factor is the most influent factor in Arabic movie reviews.Belgacem BrahimiMohamed TouahriaAbdelkamel TariElsevierarticleText miningOpinion miningSentiment classificationReview extractionCombined approachElectronic computers. Computer scienceQA75.5-76.95ENJournal of King Saud University: Computer and Information Sciences, Vol 33, Iss 10, Pp 1242-1250 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Text mining Opinion mining Sentiment classification Review extraction Combined approach Electronic computers. Computer science QA75.5-76.95
spellingShingle	Text mining Opinion mining Sentiment classification Review extraction Combined approach Electronic computers. Computer science QA75.5-76.95 Belgacem Brahimi Mohamed Touahria Abdelkamel Tari Improving sentiment analysis in Arabic: A combined approach
description	Sentiment analysis SA is concerned with determining users’ opinions from text reviews. However, mining these opinions from massive amounts of unstructured and lengthy documents is a challenging task. In this paper, we propose methods that extract valuable opinions and values from online movie reviews to improve SA in Arabic. First, we propose a method that explores the role of n-gram and skip-n-gram models in opinion classification. Second, we study a method that exploits subjective words such as adjectives and nouns by applying Part-Of Speech tagging. Both of the methods are combined with a feature reduction technique to enhance SA results. Third, we present a method that seeks to extract relevant opinions such as review summaries and conclusion opinions. Then, a combined approach is proposed to augment opinion classification results. Forth, we introduce a method for analyzing customers’ opinions by determining factors impacting their attitudes based on the costumer value model. Experimental results conducted on two datasets prove that our proposed methods are effective and provide better scores than baseline sentiment classifiers. The best obtained classification results reached 96% in F-Measure. These results indicate also that the aesthetic factor is the most influent factor in Arabic movie reviews.
format	article
author	Belgacem Brahimi Mohamed Touahria Abdelkamel Tari
author_facet	Belgacem Brahimi Mohamed Touahria Abdelkamel Tari
author_sort	Belgacem Brahimi
title	Improving sentiment analysis in Arabic: A combined approach
title_short	Improving sentiment analysis in Arabic: A combined approach
title_full	Improving sentiment analysis in Arabic: A combined approach
title_fullStr	Improving sentiment analysis in Arabic: A combined approach
title_full_unstemmed	Improving sentiment analysis in Arabic: A combined approach
title_sort	improving sentiment analysis in arabic: a combined approach
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/0433758789d94bee92da2dbf51a12e3d
work_keys_str_mv	AT belgacembrahimi improvingsentimentanalysisinarabicacombinedapproach AT mohamedtouahria improvingsentimentanalysisinarabicacombinedapproach AT abdelkameltari improvingsentimentanalysisinarabicacombinedapproach
_version_	1718418201902055424

Improving sentiment analysis in Arabic: A combined approach

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