User-generated content about brands is an important source of big data that can be transformed into valuable information. A huge number of items are reviewed and rated by consumers on a daily basis, and managers have a keen interest in real-time monitoring of this information to improve decision-making. The main challenge is to mine reliable textual consumer opinions, and automatically use them to rate the best products or brands. We propose a framework to automatically analyse these reviews, transforming negative and positive user opinions in a quantitative score. Sentiment analysis was employed to analyse online reviews on Amazon. The Fake Review Detection Framework—FRDF— detects and removes fake reviews using Natural Language Processing technology. The FRDF was tested on reviews of products from high-tech industries. Brands were rated according to consumer sentiment. The findings demonstrate that brand managers and consumers would find this tool useful, in combination with the 5-Star score, for more comprehensive decision-making. For instance, the FRDF ranks the best products by price alongside their respective sentiment value and the 5-Star score. •A novel modular framework that deals with textual information mining included in users’ reviews is proposed.•The framework uses NLP techniques including Sentiment Analysis and Fake Review Detectors tools.•Fake reviews are detected and removed because they influence negatively in the obtained results.•The application of the framework with a case study using a corpus of reviews of tech products is included in order to validate the proposal.•Dashboards generated assist managers to improve the decision-making process.