Rich morphology language, such as Arabic, requires more investigation and methods targeted toward improving the sentiment analysis task. An example of external knowledge that may provide some semantic relationships within the text is the word clustering technique. This article demonstrates the ongoing work that utilizes word clustering when conducting Arabic sentiment analysis. Our proposed method employs supervised sentiment classification by enriching the feature space model with word cluster information. In addition, the experiments and evaluations that were conducted in this study demonstrated that by combining the clustering feature with sentiment analysis for Arabic, this improved the performance of the classifier.

Service Oriented Architecture (SOA) is a standard to implement and design applications. It is a channel of communication between service consumer (SC) and service provider (SP). SP offers a service and SC uses a service. Mobile web service verifier component, of SOA architecture, matches best service that is called. Mobile web service verifier component finds a best match of particular service in SP if it is available by the SP. Therefore, there is a pressing need to develop a component that can create a service if it is not found by mobile web service verifier component. This problem is addressed by proposing a new component for SOA architecture called service upgrade (SU) to increase and improve the service creation speed and satisfy SC and SP needs. The questionnaire is used as a research design to evaluate the proposed solution by providing qualitative data. The results show that the majority of respondents are in favor of the proposed solution. It is anticipated that the proposed solution will cater the industrial problem in hand.

Sentiment analysis, also known as opinion mining, is referred to the studying and analyzing of people's opinions, emotions, sentiments, appraisals, evaluations and attitudes about an entity like an organization, product, service, issue, individual, event, topic, etc. and about their properties. It denotes a major problem part. While in Technical industry, the name, sentiment analysis is generally used, but in academic institutions, both terms, 'sentimental analysis' and 'opinion mining' are frequently used. They, unitedly represent the unique field of study. The term 'sentiment analysis' was first appeared in (Nasukawa and Yi, 2003) probably, and the term opinion mining was first appeared in (Dave, Lawrence and Pennock, 2003). In spite of the previous researches on sentiments and opinions published earlier, they become more prominent after this date.
Opinion mining and its related fields, for example, estimations, feelings, assessments, and states of mind are the subjects of investigation of slant examination and sentiment mining. The commencement and quick development of the field agree with those of the online networking on the Web, e.g., audits, gathering dialogs, web journals, microblogs, Twitter, and interpersonal organizations, in light of the fact that without precedent for mankind's history, we have an enormous volume of stubborn information recorded in computerized frames. We are working on accessing data from various data sources available online, including the various social networking sites, blogs or forums available. By using the provided APIs (of the respective site) and the web crawler mechanism, we will generate the outcomes and use them as a representation for users. Users can access the data represented in the forms of graphs and charts to analyze the behavior of the public on a particular brand, topic, issue or product.

Since the more importance is played on customer's behavior in today's business market, telecom companies are not only focusing on customer's profitability to increase their market share but also on their potential churn customers who could terminate the relation with the company in near future. Big data promises to promote growth and increase efficiency and profitability across the entire telecom value chain. This paper presents a framework for targeting high value customers and potential churn customers. Firstly, customers are segmented on basis of RFM (Recency-Frequency-Monetary) analysis and finally customers in each segment are targeted by various offers on basis of their similar characteristics.

Effective Software development is a very complex task. When this task span over large geographical regions with different cultures, it becomes very critical maintaining every task and making proper co-ordination among them. For coming decades, considering different web technologies and other emerging technologies, developers will need a robust and secure development environment for proper combination with collaboration tools having existing social and technical elements. Again this software engineering environment should ensure participation of individuals such a way that their potential get full force workable mode. This environment should also face time critical project management issues having high risk at different stages. Fast and cost effective development also requires use of off the shelf resources. In this paper, a software engineering environment is proposed for coming days considering all these issues hoping that, it will serve as a primary standard of development environment while meeting all demands of developers.

The rapid development of the transfer of data via internet made it easier to send the correct information and quicker to the destination. Today's massive demand of web applications needs the information to be communicated in a secure manner. The transmission of information in a public communication system isn't secure because of interception and improper manipulation by an eavesdropper. So for this problem, the solution is Steganography, which means the art of hiding information in ways that avert the revealing of hiding messages. Secure video steganography is a difficult task of sending the embedded data to the receiver without being detected. This paper presents a steganographic model which is based on random pattern based sequential bit encoding. The proposed model has been designed to enhance the security level. The video embedding creates the higher level of security for the embedded data. Here the encrypted secret image is embedded into the blocks of a cover video file which is selected using autocorrelation function which measures the similarity between the two images. The embedding is done on the basis of random selection of the pixels in the blocks of cover video. The performance parameters that are used to obtain results are taken in the form of chi-square histogram difference measurements. The proposed model increases the security by using random pattern embedding.