Sentiment classification is the most rising research areas of sentiment analysis and text mining, especially with the massive amount of opinions available on social media. Recent results and efforts have demonstrated that there is no single strategy can mutually accomplish the best prediction performance on various datasets. There is a lack of existing researches to Arabic sentiment analysis compared to English sentiment analysis, because of the unique nature and difficulty of the Arabic language which leads to shortage in Arabic dataset used in sentiment analysis. An Arabic benchmark dataset is proposed in this paper for sentiment analysis showing the gathering methodology of the most recent tweets in different Arabic dialects. This dataset includes more than 151,000 different opinions in variant Arabic dialects which labeled into two balanced classes, namely, positive and negative. Different machine learning algorithms are applied on this dataset including the ridge regression which gives the highest accuracy of 99.90%.

With the extraordinary maturity of data exchange in network environments and increasing the attackers capabilities, information security has become the most important process for data storage and communication. In order to provide such information security the confidentiality, data integrity, and data origin authentication must be verified based on cryptographic encryption algorithms. This paper presents a development of the advanced encryption standard (AES) algorithm, which is considered as the most eminent symmetric encryption algorithm. The development focuses on the generation of the integration between the developed AES based S-Boxes, and the specific selected secret key generated from the quantum key distribution.