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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| Format: | Article |
| Language: | English |
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Springer
2021-12-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157819303283 |
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| author | Belgacem Brahimi Mohamed Touahria Abdelkamel Tari |
| author_facet | Belgacem Brahimi Mohamed Touahria Abdelkamel Tari |
| author_sort | Belgacem Brahimi |
| collection | DOAJ |
| 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 |
| id | doaj-art-0433758789d94bee92da2dbf51a12e3d |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-0433758789d94bee92da2dbf51a12e3d2025-08-20T03:49:03ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782021-12-0133101242125010.1016/j.jksuci.2019.07.011Improving sentiment analysis in Arabic: A combined approachBelgacem Brahimi0Mohamed Touahria1Abdelkamel Tari2Department of Informatics, Faculty of Exact Sciences, University of Bejaia, 06000 Bejaia, Algeria; Corresponding author.Department of Computer Science, University of Setif1, 19000 Setif, AlgeriaLIMED Laboratory, Faculty of Exact Sciences, University of Bejaia, 06000 Bejaia, AlgeriaSentiment 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.http://www.sciencedirect.com/science/article/pii/S1319157819303283Text miningOpinion miningSentiment classificationReview extractionCombined approach |
| spellingShingle | Belgacem Brahimi Mohamed Touahria Abdelkamel Tari Improving sentiment analysis in Arabic: A combined approach Journal of King Saud University: Computer and Information Sciences Text mining Opinion mining Sentiment classification Review extraction Combined approach |
| title | 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_short | Improving sentiment analysis in Arabic: A combined approach |
| title_sort | improving sentiment analysis in arabic a combined approach |
| topic | Text mining Opinion mining Sentiment classification Review extraction Combined approach |
| url | http://www.sciencedirect.com/science/article/pii/S1319157819303283 |
| work_keys_str_mv | AT belgacembrahimi improvingsentimentanalysisinarabicacombinedapproach AT mohamedtouahria improvingsentimentanalysisinarabicacombinedapproach AT abdelkameltari improvingsentimentanalysisinarabicacombinedapproach |