Online reviews have a profound impact on the customer or “newbie” who wishes to purchase or consume a product via Web 2.0 e-commerce. Online reviews contain features that form half of the analysis in opinion mining. Most of today’s systems work on the basis of summarization, looking at the average obtained features and their sentiments, leading to structured review information being generated. Often, the context surrounding a feature, which helps the sentiment of the review to be classified clearly, is overlooked. The Web 3.0-based machine interpretable Resource Description Framework (RDF) can be used to structure these unstructured reviews into features and sentiments, which are obtained via traditional preprocessing and extraction techniques. Here, data about the context is also provided for future ontology-based analysis, with support from the WordNet lexical database for word sense disambiguation and SentiWordNet scores for sentiment word extraction. Many popular RDF vocabularies are helpful for obtaining such machine-processable data. This work forms the basis for creating/upgrading the (available) OWL Ontology that can be used as a structured data model with rich semantics for supervised machine learning. With this method, the classified sentiment categories are validated in relation to precise sentiments and are sent back to the interface in corresponding “feature/sentiment” pairs so that reviews are filtered clearly, which helps to satisfy the feature set of the customer.