The tremendous growth of visual media over social media and the internet demands automated visual content analysis. The exceptional understanding and analysis potential of machine learning and artificial intelligence models has become a game changer for the visual content analysis area. This paper proposes a web-based framework that automatically scrutinizes the content of a user-given URL. The title ScrapeSense, the term ’Scrape’ marks automated visual content extraction from the webpage, and ’Sense’ points out the system’s caliber to intelligently analyse both object and emotion from scraped images. The designed approach combines the two major models of visual content analysis, object and emotion detection. Object detection algorithm proficiently localizes multiple objects in the given complex image, and emotion detection understands and detects recognized facial expressions with predefined labels. The proposed framework delivers a user-friendly web page with an option for users to enter the URL of their choice and get emotion content and detected objects in an HTML page. The formulated method is a simple yet powerful approach devised using the Flask framework blended with web scraping. It is an effective way to interact and analyse the emotional context and object depicted in the given online digital image, proven to be a better approach than existing ones.