Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction
In the realm of natural language processing (NLP), the pivotal task of analysing affective states, including sentiment and emotion, has seen significant advancements in recent years. However, in the context of the Arabic language, studies predominantly resort to machine learning or deep learning alg...
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Mehran University of Engineering and Technology
2025-04-01
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| Series: | Mehran University Research Journal of Engineering and Technology |
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| Online Access: | https://murjet.muet.edu.pk/index.php/home/article/view/302 |
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| author | Shakeel Ahmad Sheikh Muhammad Saqib Asif Hassan Syed Nashwan Alromema Ali Kararay |
| author_facet | Shakeel Ahmad Sheikh Muhammad Saqib Asif Hassan Syed Nashwan Alromema Ali Kararay |
| author_sort | Shakeel Ahmad |
| collection | DOAJ |
| description | In the realm of natural language processing (NLP), the pivotal task of analysing affective states, including sentiment and emotion, has seen significant advancements in recent years. However, in the context of the Arabic language, studies predominantly resort to machine learning or deep learning algorithms for sentiment and emotion analysis, often neglecting the utilization of current pre-trained language models. While deep learning models tailored for Arabic text have garnered attention, there exists a considerable gap in integrating widely used tools like AFINN, TextBlob, VADER, and Pattern.en for text polarity due to compatibility issues with Arabic text. This study addresses this gap by striving to make Arabic text compatible with these dictionaries, presenting a comprehensive analysis. The findings suggest that AFINN and VADER emerge as the most suitable dictionaries for effective sentiment analysis in Arabic text. Specifically, AFINN achieved 83% accuracy, with a precision of 0.88, recall of 0.80, and an F1-score of 0.84 for negative sentiment, and a precision of 0.77, recall of 0.86, and an F1-score of 0.82 for positive sentiment. VADER demonstrated 83% accuracy, with a precision of 0.88, recall of 0.80, and an F1-score of 0.84 for negative sentiment, and a precision of 0.78, recall of 0.86, and an F1-score of 0.82 for positive sentiment. These results indicate that both AFINN and VADER are effective tools for sentiment analysis in Arabic, providing a reliable solution for text polarity classification. |
| format | Article |
| id | doaj-art-056a38df392b41a88a900da87b0daf71 |
| institution | DOAJ |
| issn | 0254-7821 2413-7219 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj-art-056a38df392b41a88a900da87b0daf712025-08-20T03:06:06ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192025-04-0144219721610.22581/muet1982.3449304Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extractionShakeel Ahmad0Sheikh Muhammad Saqib1Asif Hassan Syed2Nashwan Alromema3Ali Kararay4Department of Computer Science, College of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Computing and Information Technology, Gomal University, Dera Ismail Khan, PakistanDepartment of Computer Science, College of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Computer Science, College of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Computer Science, College of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi ArabiaIn the realm of natural language processing (NLP), the pivotal task of analysing affective states, including sentiment and emotion, has seen significant advancements in recent years. However, in the context of the Arabic language, studies predominantly resort to machine learning or deep learning algorithms for sentiment and emotion analysis, often neglecting the utilization of current pre-trained language models. While deep learning models tailored for Arabic text have garnered attention, there exists a considerable gap in integrating widely used tools like AFINN, TextBlob, VADER, and Pattern.en for text polarity due to compatibility issues with Arabic text. This study addresses this gap by striving to make Arabic text compatible with these dictionaries, presenting a comprehensive analysis. The findings suggest that AFINN and VADER emerge as the most suitable dictionaries for effective sentiment analysis in Arabic text. Specifically, AFINN achieved 83% accuracy, with a precision of 0.88, recall of 0.80, and an F1-score of 0.84 for negative sentiment, and a precision of 0.77, recall of 0.86, and an F1-score of 0.82 for positive sentiment. VADER demonstrated 83% accuracy, with a precision of 0.88, recall of 0.80, and an F1-score of 0.84 for negative sentiment, and a precision of 0.78, recall of 0.86, and an F1-score of 0.82 for positive sentiment. These results indicate that both AFINN and VADER are effective tools for sentiment analysis in Arabic, providing a reliable solution for text polarity classification.https://murjet.muet.edu.pk/index.php/home/article/view/302natural language processing (nlp)deep learningafinntextblobaderpattern.en |
| spellingShingle | Shakeel Ahmad Sheikh Muhammad Saqib Asif Hassan Syed Nashwan Alromema Ali Kararay Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction Mehran University Research Journal of Engineering and Technology natural language processing (nlp) deep learning afinn textblob ader pattern.en |
| title | Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction |
| title_full | Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction |
| title_fullStr | Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction |
| title_full_unstemmed | Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction |
| title_short | Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction |
| title_sort | exploring the best fit a comparative analysis of afinn textblob vader and pattern on arabic reviews for optimal dictionary extraction |
| topic | natural language processing (nlp) deep learning afinn textblob ader pattern.en |
| url | https://murjet.muet.edu.pk/index.php/home/article/view/302 |
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