The National Institute of Standards and Technology (NIST) defines the fundamental of cloud computing as a concept for delivering a shared pool of configurable computing resources, which can be provisioned and released with minimum management effort and service provider interaction. Also, it enables convenient, on-demand network access to these resources [1]. Most companies today are increasingly interested in taking advantage of the flexibility and choice of multiple cloud offerings and adopt more than one cloud to make the best use of a variety of services [2]. Multi-cloud is a combination of multiple public clouds and private clouds [3]. Its goal is to enable cloud users to avoid vendor blocking and make the best use of multiple cloud services that can cooperate and interact with each other [4]. In 2018, the International Data Corporation predicted that more than 85% of IT companies would invest
a b s t r a c tIt is very current in today life to seek for tracking the people opinion from their interaction with occurring events. A very common way to do that is comments in articles published in newspapers web sites dealing with contemporary events. Sentiment analysis or opinion mining is an emergent field who's the purpose is finding the behind phenomenon masked in opinionated texts. We are interested in our work by comments in Algerian newspaper websites. For this end, two corpora were used; SANA and OCA. SANA corpus is created by collection of comments from three Algerian newspapers, and annotated by two Algerian Arabic native speakers, while OCA is a freely available corpus for sentiment analysis. For the classification we adopt Supports vector machines, naïve Bayes and k-nearest neighbors. Obtained results are very promising and show the different effects of stemming in such domain, also k-nearest neighbors gives important improvement comparing to other classifiers unlike similar works where SVM is the most dominant. From this study we observe the importance of dedicated resources and methods the newspaper comments sentiment analysis which we look forward in future works. Ó 2019 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Cloud Computing refers to the use of computer resources as a service on-demand via internet. It is mainly based on data and applications outsourcing, traditionally stored on users' computers, to remote servers (datacenters) owned, administered and managed by third parts. This paper is an overview of data security issues in the cloud computing. Its objective is to highlight the principal issues related to data security that raised by cloud environment. To do this, these issues was classified into three categories: 1-data security issues raised by single cloud characteristics compared to traditional infrastructure, 2-data security issues raised by data life cycle in cloud computing (stored, used and transferred data), 3-data security issues associated to data security attributes (confidentiality, integrity and availability). For each category, the common solutions used to secure data in the cloud were emphasized.
It is a challenging task to identify sentiment polarity in Arabic journals comments. Algerian daily newspapers interest more and more people in Algeria, and due to this fact they interact with it by comments they post on articles in their websites. In this paper we propose our approach to classify Arabic comments from Algerian Newspapers into positive and negative classes. Publicly-available Arabic datasets are very rare on the Web, which make it very hard to carring out studies in Arabic sentiment analysis. To reduce this gap we have created SIAAC (Sentiment polarity Identification on Arabic Algerian newspaper Comments) a corpus dedicated for this work. Comments are collected from website of well-known Algerian newspaper Echorouk. For experiments two well known supervised learning classifiers Support Vector Machines (SVM) and Naïve Bayes (NB) were used, with a set of different parameters for each one. Recall, Precision and F_measure are computed for each classifier. Best results are obtained in term of precision in both SVM and NB, also the use of bigram increase the results in the two models. Compared with OCA, a well know corpus for Arabic, SIAAC give a competitive results. Obtained results encourage us to continue with others Algerian newspaper to generalize our model.
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