Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon
Sentiment analysis is an emerging research field that can be integrated with other domains, including data mining, natural language processing and machine learning. In political articles, it is difficult to understand and summarise the state or overall views due to the diversity and size of social m...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9336585/ |
| _version_ | 1828909881492504576 |
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| author | Jwan K. Alwan Abir Jaafar Hussain Dhafar Hamed Abd Ahmed Tariq Sadiq Mohamed Khalaf Panos Liatsis |
| author_facet | Jwan K. Alwan Abir Jaafar Hussain Dhafar Hamed Abd Ahmed Tariq Sadiq Mohamed Khalaf Panos Liatsis |
| author_sort | Jwan K. Alwan |
| collection | DOAJ |
| description | Sentiment analysis is an emerging research field that can be integrated with other domains, including data mining, natural language processing and machine learning. In political articles, it is difficult to understand and summarise the state or overall views due to the diversity and size of social media information. A number of studies were conducted in the area of sentiment analysis, especially using English texts, while Arabic language received less attention in the literature. In this study, we propose a detection model for political orientation articles in the Arabic language. We introduce the key assumptions of the model, present and discuss the obtained results, and highlight the issues that still need to be explored to further our understanding of subjective sentences. The main purpose of applying this new approach based on Rough Set (RS) theory is to increase the accuracy of the models in recognizing the orientation of the articles. We present extensive simulation results, which demonstrate the superiority of the proposed model over other algorithms. It is shown that the performance of the proposed approach significantly improves by adding discriminating features. To summarize, the proposed approach demonstrates an accuracy of 85.483%, when evaluating the orientation of political Arabic datasets, compared to 72.58% and 64.516% for the Support Vector Machines and Naïve Bayes methods, respectively. |
| first_indexed | 2024-12-13T18:34:11Z |
| format | Article |
| id | doaj.art-feab163bd4044897888ad19136d88359 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:34:11Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-feab163bd4044897888ad19136d883592022-12-21T23:35:24ZengIEEEIEEE Access2169-35362021-01-019244752448410.1109/ACCESS.2021.30549199336585Political Arabic Articles Orientation Using Rough Set Theory With Sentiment LexiconJwan K. Alwan0https://orcid.org/0000-0003-2569-4822Abir Jaafar Hussain1https://orcid.org/0000-0001-8413-0045Dhafar Hamed Abd2https://orcid.org/0000-0003-0548-0616Ahmed Tariq Sadiq3Mohamed Khalaf4Panos Liatsis5https://orcid.org/0000-0002-5490-6030Biomedical Informatics College, University of Information Technology and Communications, Baghdad, IraqDepartment of Computer Science, Liverpool John Moores University, Liverpool, U.K.Department of Computer Science, Al-Maarif University College, Ramadi, IraqDepartment of Computer Science, University of Technology, Baghdad, IraqDepartment of Computer Science, Al-Maarif University College, Ramadi, IraqDepartment of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab EmiratesSentiment analysis is an emerging research field that can be integrated with other domains, including data mining, natural language processing and machine learning. In political articles, it is difficult to understand and summarise the state or overall views due to the diversity and size of social media information. A number of studies were conducted in the area of sentiment analysis, especially using English texts, while Arabic language received less attention in the literature. In this study, we propose a detection model for political orientation articles in the Arabic language. We introduce the key assumptions of the model, present and discuss the obtained results, and highlight the issues that still need to be explored to further our understanding of subjective sentences. The main purpose of applying this new approach based on Rough Set (RS) theory is to increase the accuracy of the models in recognizing the orientation of the articles. We present extensive simulation results, which demonstrate the superiority of the proposed model over other algorithms. It is shown that the performance of the proposed approach significantly improves by adding discriminating features. To summarize, the proposed approach demonstrates an accuracy of 85.483%, when evaluating the orientation of political Arabic datasets, compared to 72.58% and 64.516% for the Support Vector Machines and Naïve Bayes methods, respectively.https://ieeexplore.ieee.org/document/9336585/Arabic political articlesupport vector machinesNaïve Bayesrough set theoryn-gramsentiment lexicon |
| spellingShingle | Jwan K. Alwan Abir Jaafar Hussain Dhafar Hamed Abd Ahmed Tariq Sadiq Mohamed Khalaf Panos Liatsis Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon IEEE Access Arabic political article support vector machines Naïve Bayes rough set theory n-gram sentiment lexicon |
| title | Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon |
| title_full | Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon |
| title_fullStr | Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon |
| title_full_unstemmed | Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon |
| title_short | Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon |
| title_sort | political arabic articles orientation using rough set theory with sentiment lexicon |
| topic | Arabic political article support vector machines Naïve Bayes rough set theory n-gram sentiment lexicon |
| url | https://ieeexplore.ieee.org/document/9336585/ |
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