Sentiment Analysis in Arabic Text and Emoji Using Deep Learning Methods
The advent of social media has simplified the rapid publishing of explanations on inclusive announcements, movies, politics, and the economy. This growth has led to an increase in the breadth of topics covered. This emotion analysis includes many aspects. Arabic and OMCD survey big data were integra...
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| Format: | Article |
| Language: | Arabic |
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University of Information Technology and Communications
2024-12-01
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| Series: | Iraqi Journal for Computers and Informatics |
| Subjects: | |
| Online Access: | https://ijci.uoitc.edu.iq/index.php/ijci/article/view/534 |
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| author | Suhad Nasrallah Taraf Ahmed T. Sadiq |
| author_facet | Suhad Nasrallah Taraf Ahmed T. Sadiq |
| author_sort | Suhad Nasrallah Taraf |
| collection | DOAJ |
| description | The advent of social media has simplified the rapid publishing of explanations on inclusive announcements, movies, politics, and the economy. This growth has led to an increase in the breadth of topics covered. This emotion analysis includes many aspects. Arabic and OMCD survey big data were integrated with data from Twitter to inform this study. Different word embedding methods were implemented, such as Spacy (W2V), FastText, and Arabic Bidirectional Encoder Representation (AraBERT). In the context of sentiment analysis models, convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) were employed. The evaluation of model performance was based on accuracy. Deep learning (DL) methods using the AST (Arabic sentiment Twitter) dataset yielded 72% and 95% model accuracy rates. The accuracy rates for the OMCD (Offensive Moroccan Comments Dataset) is a dataset containing offensive comments in the Moroccan dialect. dataset fell within the range of 54% to 84%. |
| format | Article |
| id | doaj-art-8fb6bba8691e4df19282c9c2aabc3275 |
| institution | Kabale University |
| issn | 2313-190X 2520-4912 |
| language | Arabic |
| publishDate | 2024-12-01 |
| publisher | University of Information Technology and Communications |
| record_format | Article |
| series | Iraqi Journal for Computers and Informatics |
| spelling | doaj-art-8fb6bba8691e4df19282c9c2aabc32752025-01-14T10:59:20ZaraUniversity of Information Technology and CommunicationsIraqi Journal for Computers and Informatics2313-190X2520-49122024-12-0150213214310.25195/ijci.v50i2.534497Sentiment Analysis in Arabic Text and Emoji Using Deep Learning MethodsSuhad Nasrallah Taraf0Ahmed T. Sadiq1Iraqi Commission for Computers & InformaticsUniversity of TechnologyThe advent of social media has simplified the rapid publishing of explanations on inclusive announcements, movies, politics, and the economy. This growth has led to an increase in the breadth of topics covered. This emotion analysis includes many aspects. Arabic and OMCD survey big data were integrated with data from Twitter to inform this study. Different word embedding methods were implemented, such as Spacy (W2V), FastText, and Arabic Bidirectional Encoder Representation (AraBERT). In the context of sentiment analysis models, convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) were employed. The evaluation of model performance was based on accuracy. Deep learning (DL) methods using the AST (Arabic sentiment Twitter) dataset yielded 72% and 95% model accuracy rates. The accuracy rates for the OMCD (Offensive Moroccan Comments Dataset) is a dataset containing offensive comments in the Moroccan dialect. dataset fell within the range of 54% to 84%.https://ijci.uoitc.edu.iq/index.php/ijci/article/view/534keywords: text classification; emoji analysis; sentiment analysis; deep learning; spacy |
| spellingShingle | Suhad Nasrallah Taraf Ahmed T. Sadiq Sentiment Analysis in Arabic Text and Emoji Using Deep Learning Methods Iraqi Journal for Computers and Informatics keywords: text classification; emoji analysis; sentiment analysis; deep learning; spacy |
| title | Sentiment Analysis in Arabic Text and Emoji Using Deep Learning Methods |
| title_full | Sentiment Analysis in Arabic Text and Emoji Using Deep Learning Methods |
| title_fullStr | Sentiment Analysis in Arabic Text and Emoji Using Deep Learning Methods |
| title_full_unstemmed | Sentiment Analysis in Arabic Text and Emoji Using Deep Learning Methods |
| title_short | Sentiment Analysis in Arabic Text and Emoji Using Deep Learning Methods |
| title_sort | sentiment analysis in arabic text and emoji using deep learning methods |
| topic | keywords: text classification; emoji analysis; sentiment analysis; deep learning; spacy |
| url | https://ijci.uoitc.edu.iq/index.php/ijci/article/view/534 |
| work_keys_str_mv | AT suhadnasrallahtaraf sentimentanalysisinarabictextandemojiusingdeeplearningmethods AT ahmedtsadiq sentimentanalysisinarabictextandemojiusingdeeplearningmethods |