A restaurant dinner can become a memorable experience due to an unexpected aspect that is appreciated by the customer, such as an origami-making station in the waiting area. If aspects that are atypical for a restaurant experience were known in advance, they could be leveraged to make recommendations that have the potential to engender serendipitous experiences, further increasing user satisfaction. Although relatively rare, due to their memorable quality, atypical aspects often end up being mentioned in reviews. Correspondingly, in this thesis, I propose the task of detecting atypical aspects in customer reviews. To facilitate the development of extraction models, I manually annotate benchmark datasets of reviews in three domains: restaurants, hotels, and hair salons. The datasets are then used to evaluate a number of language models, ranging from fine-tuning the instruction-based text-to-text transformer Flan-T5 to zero-shot and few-shot prompting of the much larger ChatGPT.