Aspect category detection (ACD) is one of the challenging sub-tasks in aspect-based sentiment analysis. The goal of this task is to detect implicit or explicit aspect categories from the sentences of user-generated reviews. Since annotation over the aspects is time-consuming, the amount of labeled data is limited for super-vised learning. In this paper, we study contextual representations of reviews using the BERT model to better extract useful features from text segments in the reviews, and train a supervised classifier with a small amount of labeled data for the ACD task. Experimental results obtained on Amazon reviews of six product domains show that our method is effective in some domains.