Aspect extraction plays an important role in aspect-level sentiment analysis. Most existing approaches focus on explicit aspect extraction and either seriously rely on syntactic rules or only make use of neural network without linguistic knowledge. This paper proposes a linguistic attention-based model (LABM) to implement explicit and implicit aspect extraction together. The linguistic attention mechanism incorporates the knowledge of linguistics which has proven to be very useful in aspect extraction. We also propose a novel unsupervised training approach, distributed aspect learning (DAL), the core idea of DAL is that the aspect vector should align closely to the neural word embeddings of nouns which are tightly associated with the valid aspect indicators. Experimental results using six datasets demonstrate that our model is explainable and outperforms baseline models on evaluation tasks.