At Opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as Opinion-as-a-Service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.
In the recent times the amount of data are generated and stored by various industries are rapidly increasing on the internet thus data scientists are facing a lot of challenges for maintaining a huge amount of data as the fast growing industries require the significant information for enhancing the business and for predictive analysis of the information. This paper focuses on the various states of art studies towards Big Data analytic techniques and gives a better comparative analysis of various applications proposed till date. Inference has been done for evaluating the performance efficiency, limitations and the advantages of the different types of existing Big Data Analytic techniques. The main objective of the proposed study is to provide a better and significant research perspective and an overview of data analysis techniques which are referred to the papers found on the web which will be quite helpful for the future research prospective of this domain.
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