In recent years, research into developing state-of-the-art models for Arabic natural language processing tasks has gained momentum. These models must address the added difficulties related to the nature and structure of the Arabic language. In this paper, we propose three models, a human-engineered feature-based (HEF) model, a deep feature-based (DF) model, and a hybrid of both models (HEF+DF) for emotion recognition in Arabic text. We evaluated the performance of the proposed models on the SemEval-2018, IAEDS, and AETD datasets by comparing the performances of those models on each emotion label. We also compared the model performances with those of other state-of-the-art models. The results show that the HEF+DF model outperformed the DF and HEF models on all datasets. The DF model performed better than the HEF model on the SemEval-2018 and AETD datasets, while the HEF model performed better than the DF model on the IAEDS dataset. The HEF+DF model outperformed the state-of-the-art models in terms of accuracy, weighted-average precision, weighted-average recall, and weighted-average F-score on the AETD dataset and in terms of accuracy, macro-averaged precision, macro-averaged recall, and macroaveraged F-score on the IAEDS dataset. It also achieved the best macro-averaged F-score and the second-best Jaccard accuracy and micro-averaged F-score on the SemEval-2018 dataset. INDEX TERMS Arabic natural language processing, deep learning, emotion recognition, small dataset.
Cellular phones and other hand-held devices are now extensively used to write emails, notes and long texts. However, the arrangement of keys in the current keyboards is not optimized to facilitate rapid and ergonomic typing. In this paper, we aim to optimize the Arabic keyboard layout for applications that predominantly use a single pointer. The single-finger keyboard layout problem can be modeled in terms of the Quadratic Assignment Problem (QAP), which can be solved using metaheuristic algorithms. To adapt the problem to the requirements of optimizing the single-finger keyboard, we used three measures in our objective function: the distance between pairs of letters, a weight for each row in the keyboard, and the hit direction of the finger. A Genetic Algorithm (GA) approach with two crossover types (2-point and modified uniform crossovers) and two different mutation operators (swap and insertion) was developed and thoroughly tested. The performance of the Genetic Algorithm was also compared against a Simulated Annealing (SA) algorithm using the same objective function. Moreover, we developed a Memetic Algorithm combining GA and SA to maximize the chances of obtaining good solutions. We compared our resulting optimized keyboard layouts with different existing and proposed layouts. The comparison results show that our keyboard layouts are more efficient, in terms of the optimization criteria considered, than the tested layouts. Finally, the performance of our keyboards was tested by virtually estimating the speed of typing. Our keyboards also outperform other layouts in terms of the measured typing speed. The details of the algorithms and the experimental results are reported in this paper.
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