Modified Seagull Optimization With Deep Learning for Affect Classification in Arabic Tweets
Arabic is one of the world’s most widely spoken languages, and there is a huge amount of digital content available in Arabic. By the categorization of Arabic documents, it becomes easier to search and access specific content of interest. With the increasing quantity of user-generated cont...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10235322/ |
| _version_ | 1797680932254121984 |
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| author | Badriyya B. Al-Onazi Hassan Alshamrani Fatimah Okleh Aldaajeh Amira Sayed A. Aziz Mohammed Rizwanullah |
| author_facet | Badriyya B. Al-Onazi Hassan Alshamrani Fatimah Okleh Aldaajeh Amira Sayed A. Aziz Mohammed Rizwanullah |
| author_sort | Badriyya B. Al-Onazi |
| collection | DOAJ |
| description | Arabic is one of the world’s most widely spoken languages, and there is a huge amount of digital content available in Arabic. By the categorization of Arabic documents, it becomes easier to search and access specific content of interest. With the increasing quantity of user-generated content on social media platforms and online forums, text classification becomes important for content filtering and moderation. Text classification on an Arabic corpus has broad applications, ranging from information retrieval and content moderation to sentiment analysis and machine translation. It enables efficient organization, analysis, and utilization of Arabic text data, contributing to various industries and domains. Therefore, this study develops a Modified Seagull Optimization with Deep Learning based Affect Classification on Arabic Tweets (MSGODL-ACAT) technique. The goal of the MSGODL-ACAT approach lies in the recognition and categorization of effects or emotions that exist in Arabic tweets. At the preliminary level, the MSGODL-ACAT technique preprocesses the input data to make the Arabic tweets into a meaningful format. Next, the Glove technique is used for the word embedding process. Moreover, the MSGODL-ACAT technique makes use of the deep belief network (DBN) method for affect categorization. At last, the MSGO algorithm is used for the optimal hyperparameter tuning of the DBN method which in turn enhances the classification results. The experimental evaluation of the MSGODL-ACAT approach is evaluated using Arabic tweets databases. The experimental outcomes signify the effectual performance of the MSGODL-ACAT algorithm over other current approaches. |
| first_indexed | 2024-03-11T23:37:27Z |
| format | Article |
| id | doaj.art-f2797e9139e54f66ab1e0db3c0d220e2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T23:37:27Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f2797e9139e54f66ab1e0db3c0d220e22023-09-19T23:01:58ZengIEEEIEEE Access2169-35362023-01-0111989589896810.1109/ACCESS.2023.331087310235322Modified Seagull Optimization With Deep Learning for Affect Classification in Arabic TweetsBadriyya B. Al-Onazi0Hassan Alshamrani1Fatimah Okleh Aldaajeh2Amira Sayed A. Aziz3Mohammed Rizwanullah4Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Teachers Training, Arabic Linguistics Institute, King Saud University, Riyadh ZIP, Saudi ArabiaDepartment of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Digital Media, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, EgyptDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaArabic is one of the world’s most widely spoken languages, and there is a huge amount of digital content available in Arabic. By the categorization of Arabic documents, it becomes easier to search and access specific content of interest. With the increasing quantity of user-generated content on social media platforms and online forums, text classification becomes important for content filtering and moderation. Text classification on an Arabic corpus has broad applications, ranging from information retrieval and content moderation to sentiment analysis and machine translation. It enables efficient organization, analysis, and utilization of Arabic text data, contributing to various industries and domains. Therefore, this study develops a Modified Seagull Optimization with Deep Learning based Affect Classification on Arabic Tweets (MSGODL-ACAT) technique. The goal of the MSGODL-ACAT approach lies in the recognition and categorization of effects or emotions that exist in Arabic tweets. At the preliminary level, the MSGODL-ACAT technique preprocesses the input data to make the Arabic tweets into a meaningful format. Next, the Glove technique is used for the word embedding process. Moreover, the MSGODL-ACAT technique makes use of the deep belief network (DBN) method for affect categorization. At last, the MSGO algorithm is used for the optimal hyperparameter tuning of the DBN method which in turn enhances the classification results. The experimental evaluation of the MSGODL-ACAT approach is evaluated using Arabic tweets databases. The experimental outcomes signify the effectual performance of the MSGODL-ACAT algorithm over other current approaches.https://ieeexplore.ieee.org/document/10235322/Affect analysisArabic tweetstext classificationnatural language processingemotion classification |
| spellingShingle | Badriyya B. Al-Onazi Hassan Alshamrani Fatimah Okleh Aldaajeh Amira Sayed A. Aziz Mohammed Rizwanullah Modified Seagull Optimization With Deep Learning for Affect Classification in Arabic Tweets IEEE Access Affect analysis Arabic tweets text classification natural language processing emotion classification |
| title | Modified Seagull Optimization With Deep Learning for Affect Classification in Arabic Tweets |
| title_full | Modified Seagull Optimization With Deep Learning for Affect Classification in Arabic Tweets |
| title_fullStr | Modified Seagull Optimization With Deep Learning for Affect Classification in Arabic Tweets |
| title_full_unstemmed | Modified Seagull Optimization With Deep Learning for Affect Classification in Arabic Tweets |
| title_short | Modified Seagull Optimization With Deep Learning for Affect Classification in Arabic Tweets |
| title_sort | modified seagull optimization with deep learning for affect classification in arabic tweets |
| topic | Affect analysis Arabic tweets text classification natural language processing emotion classification |
| url | https://ieeexplore.ieee.org/document/10235322/ |
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