Abstract-Big data is currently one of the most critical emerging technologies. Big Data are used as a concept that refers to the inability of traditional data architectures to efficiently handle the new data sets. The 4V's of big data -volume, velocity, variety and veracity makes the data management and analytics challenging for the traditional data warehouses. It is important to think of big data and analytics together. Big data is the term used to describe the recent explosion of different types of data from disparate sources. Analytics is about examining data to derive interesting and relevant trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models. Cloud computing seems to be a perfect vehicle for hosting big data workloads. However, working on big data in the cloud brings its own challenge of reconciling two contradictory design principles. Cloud computing is based on the concepts of consolidation and resource pooling, but big data systems (such as Hadoop) are built on the shared nothing principle, where each node is independent and self-sufficient. The integrating big data with cloud computing technologies, businesses and education institutes can have a better direction to the future. The capability to store large amounts of data in different forms and process it all at very large speeds will result in data that can guide businesses and education institutes in developing fast. Nevertheless, there is a large concern regarding privacy and security issues when moving to the cloud which is the main causes as to why businesses and educational institutes will not move to the cloud. This paper introduces the characteristics, trends and challenges of big data. In addition to that, it investigates the benefits and the risks that may rise out of the integration between big data and cloud computing.

Abstract-Sentiment Analysis (SA) or Opinion Mining is the process of analysing natural language texts to detect an emotion or a pattern of emotions towards a certain product to make a decision about that product. SA is a topic of text mining, Natural Language Processing (NLP) and web mining disciplines. Research in SA is currently at its peak given the amount of data generated from social media networks. The concept is that consumers are expressing exactly what they need, want and expect from a product but on the other hand the companies don't have the tools to analyse and understand these feelings to satisfy these consumers accordingly.One of the applications that generate a high rate of reactions and sentiments in social networks is Instagram. This study focuses on analysing the reactions generated by the top 50 fashion houses on Instagram given their top 20 images with the highest number of likes. The approach taken in this study is to qualify the visual aesthetics of fashion images and to establish why some succeed on social media more than others.The basic question asked in this paper is whether there are certain visual aesthetics that appeal more to the user and are therefore more successful on social media than others as determined by a measure we introduce, 'Social Value'. To do so, a sentiment analysis tool is developed to measure the proposed social value of each image. An input of comments from each image will be processed. Each comment will go through a pre-processing phase; each word will be placed through a lexicon to identify if it is positive or negative. The output of 66