Sentiment analysis (SA), also known as opinion mining, is a growing important research area. Generally, it helps to automatically determine if a text expresses a positive, negative or neutral sentiment. It enables to mine the huge increasing resources of shared opinions such as social networks, review sites and blogs. In fact, SA is used by many fields and for various languages such as English and Arabic. However, since Arabic is a highly inflectional and derivational language, it raises many challenges. In fact, SA of Arabic text should handle such complex morphology. To better handle these challenges, we decided to provide the research community and Arabic users with a new efficient framework for Arabic Sentiment Analysis (ASA). Our primary goal is to improve the performance of ASA by exploiting deep learning while varying the preprocessing techniques. For that, we implement and evaluate two deep learning models namely convolutional neural network (CNN) and long short-term memory (LSTM) models. The framework offers various preprocessing techniques for ASA (including stemming, normalisation, tokenization and stop words). As a result of this work, we first provide a new rich and publicly available Arabic corpus called Moroccan Sentiment Analysis Corpus (MSAC). Second, the proposed framework demonstrates improvement in ASA. In fact, the experimental results prove that deep learning models have a better performance for ASA than classical approaches (support vector machines, naive Bayes classifiers and maximum entropy). They also show the key role of morphological features in Arabic Natural Language Processing (NLP).

Nowadays, many industries and government can exploit Big Data to extract valuable insight. Such insight can help decision makers to enhance their strategies and optimize their plans. It helps the organization to gain a competitive advantage and provides added value for many economic and social sectors. In fact, several governments have launched programs, with important funds, in order to enhance research and development in the field of Big Data. Private sector has also made many investments to maximize profits and optimize resources. This article presents several Big Data projects, opportunities, examples and models in many sectors such as healthcare, commerce, tourism and politics. It gives also examples of technologies and solutions developed to face Big Data challenges.

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The value of Big Data is now being recognized by many industries and governments. The efficient mining of Big Data enables to improve the competitive advantage of companies and to add value for many social and economic sectors. In fact, important projects with huge investments were launched by several governments to extract the maximum benefit from Big Data. The private sector has also deployed important efforts to maximize profits and optimize resources. However, Big Data sharing brings new information security and privacy issues. Traditional technologies and methods are no longer appropriate and lack of performance when applied in Big Data context. This chapter presents Big Data security challenges and a state of the art in methods, mechanisms and solutions used to protect data-intensive information systems.

The education sector has never been so shaken up as much as this past year. COVID-19 has imposed new rules. Several countries were forced to switch overnight from a traditional educational model to a full eLearning one. Like most other countries, the Moroccan government decided to promote distance learning by implementing several initiatives, though they remained at an embryonic stage. To contribute to the movement of transforming the national educational landscape, we aimed to develop a solution that will leverage the technological advances in this field and influence the ways students learn. This will be possible by providing learners with the latest features enabling online and adaptive learning modes. Hence, the purpose of this first study is to provide an empirical evaluation of the existing open source Ed-tech projects, which will serve as the basis for the development of our global adaptive eLearning solution. Unlike existing work, which is based on literature reviews to compare the existing adaptive eLearning platforms, we have used the OpenBRR assessment methodology as a comparison methodology due to its flexibility and ease of use. This work will help us to understand the concepts of adaptivity in education. It will also describe the most popular open source Maturity Models as well as provide a clear idea about the differences between these Ed-tech open source solutions.