"You eat with your eyes first": Optimizing Yelp Image Advertising
This work addresses the challenge for businesses and marketers in optimizing online image advertising to improve customer perception and reviews, though it is incremental as it applies existing methods to a specific dataset.
The study tackled the problem of predicting business success from online images by using Yelp data to train a classifier for star ratings and a GAN to analyze effective image properties, achieving 90-98% accuracy in classification and identifying features like blue skies and open surroundings as correlated with higher reviews.
A business's online, photographic representation can play a crucial role in its success or failure. We use Yelp's image dataset and star-based review system as a measurement of an image's effectiveness in promoting a business. After preprocessing the Yelp dataset, we use transfer learning to train a classifier which accepts Yelp images and predicts star-ratings. Additionally, we then train a GAN to qualitatively investigate the common properties of highly effective images. We achieve 90-98% accuracy in classifying simplified star ratings for various image categories and observe that images containing blue skies, open surroundings, and many windows are correlated with higher Yelp reviews.