Visual imagery published on social media provides a treasure trove of information that can be mined by brands and advertisement companies to understand how brands and products are portrayed. This thesis project aims at producing visualizations, based on established semiotic models, for product comparison. To achieve this goal, two components are required: adeep learning-based model for semantic understanding and a component for brand/logo detection. For the latter, brand logos and product names are extracted from advertisement images using deep learning techniques and the Google Cloud Vision API. To identify individual product names, even under noisy or cluttered image settings, the system combines logo identification with OCR-based text recognition, employing strategies such n-gram matching and Levenshtein distance.