The subject of one’s opinions expressed in textual data provides rich information regarding their attitudes and behaviors. Many natural language processing tasks leverage such information to, for example, study product purchasing behaviors or extract insights during global events. The task of identifying these subjects is referred to as aspect extraction . Aspect extraction approaches typically focus on the identification of explicitly stated aspects in a text sample. However, it is suggested that implicit aspects , or those that must be inferred by the context provided in the text, comprise more than 20% of all aspects in a given dataset and that identification of implicit aspects is important for accurate aspect-based analyses such as aspect-based sentiment analysis. As such, this article surveys recent work in implicit aspect extraction. We define and describe commonly used datasets and algorithmic approaches and detail various challenges that have thus far led to limited research in implicit aspect extraction as compared to explicit aspect extraction, like fewer benchmark datasets and limited use of powerful attention models.