E-Shops reviews have become a valuable source of opinions, feeling, and experience by existing users which marks the failure and success of products and services. A set of favorable reviews proves that the product or service has vended well and users are pleased with them, and opposing reviews effects adversely. These reviews provide worthwhile information that might be used by prospective users to find sentiments of previously preferred users before making any decision or transaction to buy right product or service from E-shops. Moreover, service providers, vendors or manufacturers also make use of these reviews to discover public opinion, as well as limitations of their products or services. But, the basic fact about underlying online reviews shows something else. These reviews might be spurious, posted or written with hidden purposes, they often involve helpful or favorable opinion in order to boost, promote, and publicize their products and services, or with pessimistic intention to harm opponent prestige and business as well. So, the exploration and investigation of such reviews before opinion mining is significant. In this paper, a methodology is proposed which includes reviews acquisition using the Tag path clustering approach about mobile devices of different make along with metadata from Flipkart, spurious reviews detection based on identical and nearly identical reviews using semantic similarity with review length. Further, an opinion mining process is carried out by using the lexical database (SentiWordNet) approach for the computation of sentimental degree and orientation of both spurious and legitimate reviews.