In e-commerce websites and related micro-blogs, users supply online reviews expressing their preferences regarding various items. Such reviews are typically in the textual comments form, and account for a valuable information source about user interests. Recently, several works have used review texts and their related rich information like review words, review topics and review sentiments, for improving the rating-based collaborative filtering recommender systems. These works vary from one another on how they exploit the review texts for deriving user interests. This paper provides a detailed survey of recent works that integrate review texts and also discusses how these review texts are exploited for addressing some main issues of standard collaborative filtering algorithms.

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).

NoSQL solutions have been created to respond to many issues encountered when dealing with some specific applications, e.g., storage of very large datasets. In fact, traditional RDMS ensure data integrity and transaction consistency. But, this is at the cost of a rigid storage schema and a complex management. Certainly, data integrity and consistency are required in many cases like in financial applications but they are not always needed. The goal of this paper is to establish a precise picture about NoSQL's evolution and mechanisms as well as the advantages and disadvantages of the main NoSQL data models and frameworks. For this purpose, first, a deep comparison between SQL and NoSQL databases is presented. Many criteria are examined such as: scalability, performance, consistency, security, analytical capabilities and fault-tolerance mechanisms. Second, the four major types of NoSQL databases are defined and compared: key-value stores, document databases, column-oriented databases and graph databases. Third, we compare for each NoSQL data model the main available technical solutions.