E-commerce platform has a great influence on the growth of digital economy in Indonesia. This promising sector creates a fierce competition between e-commerce platforms. User reviews can be utilized to discover useful information for both developers and users. Developers use them for enhancing their application, while users take them as a consideration for using the application. We perform aspect category detection to retrieve the important aspects in reviews. We gather and analyze any aspect categories related to e-commerce and find that new potential aspects can be obtained from mobile application reviews, such as promos and payment. For identifying the aspects, we employ two different approaches, one-vs-all and single model, for 3748 annotated reviews. In one-vs-all we compare Naive Bayes, SVM, and the effect on using class-weights in SVM, since the distribution in the dataset is imbalanced. Meanwhile, we implement neural networks architecture for single model. We compare CNN to GRU+CNN architecture. GRU+CNN successfully achieves overall best result on both example-based and label-based performance metrics for multi-label task in our dataset. In example-based we use Hamming score to calculate the accuracy where GRU+CNN gets 71%, and it obtains 78% samples average F1-score for labelbased metric.