At present, the cloud computing is emerging technology to run the large set of data capably, and due to fast data growth, processing of large scale data is becoming a main point of information method and customers can estimate the quality of brands of products employing the information given by new digital marketing channels in social media. Thus, every enterprise requires finding and analyzing a big amount of digital data in order to develop their reputation among the customers. Therefore, in this paper, SLA (Service Level Agreement) based BDAAs (Big Data Analytic Applications) using Adaptive Resource Scheduling and big data with cloud based sentiment analysis is proposed to provide the deep web mining, QoS and to analyze the customer behaviors about the product. In this process, the spatio-temporal compression technique can be applied to data compression for reduction of big data. The data is classified in to positive, negative or neutral by employing the SVM with lexicon dictionary based on the customers' behaviors about brand or products. In cloud computing environment, complex to the reduction of resources cost and fluctuation of resource requirements with BDAAs. As a result, it is needed to have a common Analytics as a Service (AaaS) platform that provides a BDAAs to customers in different fields as unpreserved services in a simple to utilize a way with lower cost. Therefore, SLA based BDAAs is developed to utilize the adaptive resource scheduling depending on the customer behaviors and it can provide visualization and data integrity. Our method can give privacy of cloud owner's information with help of data integrity and authentication process. Experimental results of proposed system shows that the sentiment analysis method for online product using cloud based big data is able to classify the opinions of customers accurately and effective of the algorithm in guarantee of SLA.