Byarugaba, YonahWang, BingyangGarg, Rajiv2024-12-262024-12-262025-01-07978-0-9981331-8-810675d74-f7ca-4c6c-9f3c-a33c69540b46https://hdl.handle.net/10125/109312“Pictures are worth a thousand words," yet most platforms like Yelp, Google Maps, Instagram, Walmart, and Amazon require users to provide text, ratings, and images. Images often capture a user's intent, and the features within the images typically correlate with that intent. In this paper, we extract various features from images (such as edge distribution, color distribution, text within the image, focus, etc.) and compare simple vs. complex models to predict the ratings associated with these images. We find that features such as brightness and contrast significantly explain the rating at image-level, and models such as random forest and logistic regression provide a 0.84 F-1 score when predicting the rating. In the era of generative AI, we anticipate that sharing an image will allow platforms to auto-generate user intent and image ratings, thereby simplifying the dissemination of information.9Attribution-NonCommercial-NoDerivatives 4.0 InternationalCrowd-based Platformse-commerce, explainable machine learning, image processing, online reviews, user-generated contentConference Paper