Abstract Aspect term extraction (ATE) and opinion target extraction (OTE) are two important tasks in fine-grained sentiment analysis field. Existing approaches to ATE and OTE are mainly based on rules or machine learning methods. Rule-based methods are usually unsupervised, but they can’t make use of high level features. Although supervised learning approaches usually outperform the rule-based ones, they need a large number of labeled samples to train their models, which are expensive and time-consuming to annotate. In this paper, we propose a hybrid unsupervised method which can combine rules and machine learning methods to address ATE and OTE tasks. First, we use chunk-level linguistic rules to extract nominal phrase chunks and regard them as candidate opinion targets and aspects. Then we propose to filter irrelevant candidates based on domain correlation. Finally, we use these texts with extracted chunks as pseudo labeled data to train a deep gated recurrent unit (GRU) network for aspect term extraction and opinion target extraction. The experiments on benchmark datasets validate the effectiveness of our approach in extracting opinion targets and aspects with minimal manual annotation.