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We propose Image Content Appeal Assessment (ICAA), a novel metric focused on quantifying the level of positive interest an image's content generates for viewers, such as the appeal of food in a photograph. This new metric is fundamentally different from traditional Image-Aesthetics Assessment (IAA), which judges an image's artistic quality. While previous studies have often confused the concepts of ``aesthetics'' and ``appeal,'' our work addresses this oversight by being the first to study ICAA explicitly. To do this, we propose a novel system that automates dataset creation, avoids extensive manual labeling work, and implements algorithms to estimate and boost content appeal. We use our pipeline to generate two large-scale datasets (70K+ images each) in diverse domains (food and room interior design) to train our models, which revealed little correlation between content appeal and aesthetics. Our user study, with more than 76% of participants preferring the appeal-enhanced images, confirms that our appeal ratings accurately reflect user preferences, establishing ICAA as a unique evaluative criterion.