Opinion mining is a subset of Natural Language Processing where the customer review provided to a product is classified and ranked. Structured, Semi-Structured, and Unstructured reviews are scraped by web crawlers from the e-commerce application in the form of documents, sentences, and words. Customer reviews of the product are extracted. Myriad of semantic methods have been used for analysis on subjectivity analysis and sentiment analysis, but instead, opinion extraction based on aspect level has gained less focus in literatureduetofuzzyfactorsinopinionsuchasopinionunpredictabilityandincompleteness. This work devises a unique deep integrative framework to perform opinion classification and opinion ranking of online product reviews leveraging fuzzy logic. The uncertainty is minimized by using fuzzy logic technique. Sentiment classification and Sentiment Score is computed to the each tokenized word vector as positive word vector and negative word vector. Feature vector is generated on the generated scores. Fuzzy rules are used for opinion classification and opinion ranking on significant aspect of the product review represented in the feature map. Experimental analysis of the current model is analyzed using Amazon product review dataset crawled from Amazon ecommerce application. Dataset contains 35,000 customer reviews for product reviews. Validation is implemented to evaluate the performance of the framework. It is verified that proposed architecture provides excellent outcomes against conventional approaches.