Opinion mining is the technique of analyzing the sentiment, behavior, feelings, emotions, and attitudes of customers about a product, topic, comments on social media, etc. Online shopping has revolutionized the way customers do shopping. The customer likes to visit the online store to find their product of interest. It is becoming more difficult for customers to make purchasing decisions solely based on photos and product descriptions. Customer reviews provides a rich source of information to compare products and make purchasing decisions commonly on the basis of other customer reviews. Clients provide comments in the language of their choice, e.g. the people of Pakistan use Roman script based the Urdu language. Normally such comments are free from scripting rules. Hundreds of comments are given on a single product, which may contain noisy comments. Identifying noisy comments and finding the polarity of these comments is an active area of research. Limited research is being carried out on roman Urdu sentiment analysis. In this research paper, we propose a novel approach by using Boolean rules for the identification of the related and non-related comments. Related reviews are those which show the behavior of a customer about a particular product. Lexicons are built for the identification of noise, positive and negative reviews. The precision of the evaluation results is 68%, recall is also 68% and F-measure is 68%. Ṫhe accuracy of the whole evaluation is 60%.