Use of mobile has increased day by day. Nowadays, the mobile users prefer to use smartphones to access all types of mobile Application. These smartphones are converted from phone to smartphone by using inbuilt or downloaded mobile Applications. Generally, users download mobile Applications depending on how many users already have downloaded that Application?, What are the ratings and reviews?, What are the comments?, etc. Ranking fraud in the mobile App market refers to false or wrong activities which have a reason of pushing up the Apps on the popularity list. Certainly, it becomes more frequent for App developers to use fraud means, such as increase their App's sales or posting fake App ratings, to commit ranking fraud. There are very less understanding and analysis on how to prevent this ranking misrepresentation. Moreover, we demonstrate an optimization-based aggregation method for ranking extortion and ranking misrepresentation recognition framework for versatile Apps. It is divided into three parts: 1) ranking based evidence, 2) rating based evidence and 3) review based evidence, by demonstrating Apps' ranking, rating and survey practices through measurable theories tests. They also works on an optimization based aggregation method to coordinate every of the confirmations for misrepresentation location. We have used here opinion analysis for finding how much a review is positive or negative. This review score is used to enhance the rating score of the user and the emoticons in the reviews or comments. It has been given special weight to enhance the review score. User has provided the rating, review & comments. We are using all of them to increases the accuracy of the final rating score.