In this paper, we aim to jointly extract aspects and aspect-specific sentiment knowledge from online reviews, where the sentiment knowledge refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities. To this end, we propose a Joint Aspect/Sentiment model (JAS). JAS detects aspect-specific opinion words by integrating opinion word lexicon knowledge to explicitly separate opinion words from factual words. More importantly, JAS exploits sentiment prior and aspect-contextual sentence-level co-occurrences of opinion words in reviews to further identify aspect-aware sentiment polarities for the opinion words. We apply the learned aspect-specific sentiment knowledge to practical aspect-level sentiment analysis tasks. Experimental results show the effectiveness of JAS in learning aspect-specific sentiment knowledge and the practical value of this knowledge when applied to aspect-level sentiment classification.