Sarcasm　is　common　in　social　media,　and　people　use　it　to　express　their　opinions　with　stronger　emotions　indirectly.　Although　it　belongs　to　a　branch　of　sentiment　analysis,　traditional　sentiment　analysis　methods　cannot　identify　the　rhetoric　of　irony　as　it　requires　a　significant　amount　of　background　knowledge.　Existing　sarcasm　detection　approaches　mainly　focus　on　analyzing　the　text　content　of　sarcasm　using　various　natural　language　processing　techniques.　It　is　argued　herein　that　the　essential　issue　for　detecting　sarcasm　is　examining　its　context,　including　sentiments　of　texts　that　reply　to　the　target　text　and　user＇s　expression　habit.　A　dual-channel　convolutional　neural　network　is　proposed　that　analyzes　not　only　the　semantics　of　the　target　text,　but　also　its　sentimental　context.　In　addition,　SenticNet　is　used　to　add　common　sense　to　the　long　short-term　memory　(LSTM)　model.　The　attention　mechanism　is　then　applied　to　take　the　user＇s　expression　habits　into　account.　A　series　of　experiments　were　carried　out　on　several　public　datasets,　the　results　of　which　show　that　the　proposed　approach　can　significantly　improve　the　performance　of　sarcasm　detection　tasks.