In this paper we have addressed the problem of automatic identification of product features from customer reviews. Costumers, retailors, and manufacturers are popularly using customer reviews on websites for product reputation and sales forecasting. Opinion mining application have been potentially employed to summarize the huge collectionof customer reviews for decision making. In this paper we have proposed hybrid dependency patterns to extract product features from unstructured reviews. The proposed dependency patterns exploit lexical relations and opinion context to identify features. Based on empirical analysis, we found that the proposed hybrid patterns provide comparatively more accurate results. The average precision and recall are significantly improved with hybrid patterns.