Sentiment Analysis is a very challenging and important task that contains natural language processing, web mining and machine learning. Up to date, few researches have been conducted on sentiment classification for Arabic languages due to the lack of resources for managing sentiments or opinions such as senti-lexicons and opinion corpora. The main obstacle in Arabic sentiment analysis is that p...

The text classification process has been extensively investigated in various languages, especially English. Text models are vital several Natural Language Processing (NLP) applications. Arabic language a lot of significance. For instance, it is the fourth mostly-used on internet and sixth official United Nations. However, there few studies Arabic. A have published earlier language. In general, ...

The number of social media users has increased. These share and reshare their ideas in posts this information can be mined used by decision-makers different domains, who analyse study user opinions on networks to improve the quality products or specific phenomena. During COVID-19 pandemic, was make decisions limit spread disease using sentiment analysis. Substantial research topic been done; ho...

In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preproces-sor of NN to reduce the data in terms of both size as well as dimensionality so that the input data become more classifiable and faster for the convergence of the training process used in the NN model. To test the effectiveness ...

Exploratory data analysis over foreign language text presents virtually untapped opportunity. This work incorporates Naïve Bayes classifier with Case-Based Reasoning in order to classify and analyze Arabic texts related to fanaticism. The Arabic vocabularies are converted to equivalent English words using conceptual hierarchy structure. The understanding process operates at two phases. At the f...

In this paper, deep learning framework is proposed for text sentiment classification in Arabic. Four different architectures are explored. Three are based on Deep Belief Networks and Deep Auto Encoders, where the input data model is based on the ordinary Bag-of-Words, with features based on the recently developed Arabic Sentiment Lexicon in combination with other standard lexicon features. The ...

In this paper, we present an original approach for text summarization using conceptual data classification. We show how a given text can be summarized without losing meaningful knowledge and without using any semantic or grammatical concepts. In fact, concept date classification is used to extract the most interacting sentences from the main text and ignoring the other meaningless sentences in ...

In this paper, we present a generic Optical Character Recognition system for Arabic script languages called Nabocr. Nabocr uses OCR approaches specific for Arabic script recognition. Performing recognition on Arabic script text is relatively more difficult than Latin text due to the nature of Arabic script, which is cursive and context sensitive. Moreover, Arabic script has different writing st...

The development of an efficient compression scheme to process the Arabic language represents a difficult task. This paper employs the dynamic Huffman coding on data compression with variable length bit coding, on the Arabic language. Experimental tests have been performed on both Arabic and English text. A comparison is made to measure the efficiency of compressing data results on both Arabic a...

Existing steganography methods are still lacking in terms of capacity. Hence, a new steganography method for Arabic text is proposed. The method hides secret information bits within Arabic letters using two features, which are the moon and sun letters and the redundant Arabic extension character “-” known as Kashida. The Arabic alphabet contains 28 letters, which are classified into 14 sun lett...