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Sentiment classification is the process of classifying emotions and opinions in texts. In this study, the problem of Arabic sentiment analysis was addressed. A support vector machine (SVM) model was proposed to classify opinions in Arabic micro-texts as being positive or negative. To evaluate the performance of the SVM model, a dataset was built from tweets discussing several social issues in Saudi Arabia. These issues include changes that were implemented by the country as part of a newly established vision, known as Saudi Arabia Vision 2030. The constructed dataset was manually annotated according to the sentiment conveyed in the text. To achieve the best sentiment classification accuracy, several procedures were implemented within the proposed framework including light stemming, feature extraction (Ngrams, emoji and tweet-topic features), parameter optimisation and feature-set reduction. The experimental results revealed excellent outcomes. An accuracy of 89.83% was achieved using the proposed SVM model.

In this paper, an off-line method, based on hidden Markov model, HMM, is used for holistic recognition of handwritten words of a limited vocabulary. Three feature sets based on image gradient, black–white transition and contour chain code are used. For each feature set an HMM is trained for each word. In the recognition step, the outputs of these classifiers are combined through a multilayer perceptron, MLP. High number of connections in this network causes a computational complexity in the training. To avoid this problem, a new method is proposed. In the experiments on 16000 images of 200 names of Iranian cities, from “Iranshahr 3” dataset, the results of the proposed method are presented and compared with some similar methods. An error analysis on these results is also provided.

Arabic script is naturally cursive and unconstrained and, as a result, an automatic recognition of its handwriting is a challenging problem. The analysis of Arabic script is further complicated in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. In this paper, we introduce a new approach that performs online Arabic word recognition on a continuous word-part level, while performing training on the letter level. In addition, we appropriately handle delayed strokes by first detecting them and then integrating them into the word-part body. Our current implementation is based on Hidden Markov Models (HMM) and correctly handles most of the Arabic script recognition difficulties. We have tested our implementation using various dictionaries and multiple writers and have achieved encouraging results for both writer-dependent and writer-independent recognition.

The focus on interhemispheric interaction and integration has become a prominent aspect of laterality research. The aim of the present behavioral study was to determine whether hemisphere advantage differs between language groups. This was done by comparing how hemisphere advantage affects interhemispheric integration in monolingual and in bilingual individuals. Sixty university students (20 English monolinguals, 20 Hebrew bilinguals, and 20 balanced Arabic bilinguals) participated in two experiments, in which a lexical decision task was performed in the left and/or right visual field. Stimuli were presented unilaterally and bilaterally, whereby participants were cued to respond to the stimuli. In Experiment 1, all three groups showed an effect of lexicality, that is, participants responded to word stimuli faster than to non-word stimuli, with the Hebrew and Arabic groups showing a word advantage in spotting errors. In addition, all groups except the Hebrew group showed the expected right visual field advantage in accuracy, and the English group demonstrated this advantage in reaction time as well. In Experiment 2, responses to non-word stimuli were equally accurate in the left and right visual fields, but reaction time were faster for stimuli presented in the left visual field. The performance of balanced bilingual Arabic and unbalanced bilingual Hebrew reading groups was significantly better in the bilateral condition than in the unilateral condition. The results supported the notion that bilingual individuals show more effective interhemispheric communication and that they enjoy relative superiority in their interhemispheric processing in response to task demands.