The mining opinion from online reviews has become an increasingly urgent activity and has attracted a great deal of attention from researchers. To extract and analyze opinions from online reviews, it is unsatisfactory to only obtain the overall sentiment about a product. This project proposes an approach based on the partially-supervised alignment model, which regards identifying opinion relations as an alignment process. Then, a graph-based co-ranking algorithm is exploited to estimate the confidence of each candidate. Finally, candidates with higher confidence are extracted as opinion targets or opinion words.