Machine learning and emoji prediction: How much accuracy can MARBERT achieve?
This study investigates Machine Learning (ML) in the prediction of emojis in Arabic tweets employing the (state-of-the-art) MARBERT model. A corpus of 11379 CA tweets representing multiple Arabic colloquial dialects was collected from X.com via Python. A net dataset includes 8695 tweets, which were utilized for the analysis. These tweets were then classified into 14 categories, which were numerically encoded and used as labels. A preprocessing pipeline was designed as an interpretable baseline, allowing us to examine the relationship between lexical features and emoji categories. MARBERT was finetuned to predict emoji use from textual input. We evaluated the model performance in terms of ...
| Authors | Mohammed Q. Shormani, Ibrahim Abdulmalik Hassan Muneef Y. Alshawsh |
|---|---|
| Primary category | cs.CL |
| Published | 22 April 2026 |
| Last updated at source | 24 April 2026 |
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