Sentiment-objects　Extraction　aims　to　identify　the　targets　of　opinion　described　in　sentiment　sentence.　Previous　research　fails　to　deal　with　the　long-distance　dependencies　in　Chinese　sentences　such　as　opinion　targets　repeated　and　echo　of　the　different　part　of　sentence.　In　this　paper,　we　describe　a　probabilistic　approach　that　incorporates　the　long-distance　dependencies　to　identify　opinion　targets.　The　skip-chain　Conditional　Random　Fields　(CRFs)　is　used　to　model　the　long　distance　dependencies　between　sentiment　sentences　such　as　the　repeated　word　and　similar　expression.　Experiments　show　that　our　method　outperforms　linear-chain　CRFs　based　method,　and　it　is　effective　to　identify　opinion　targets　from　Chinese　sentences.　©　2012　IEEE.