To　tackle　the　problems　of　the　underutilization　of　context,　the　sparseness　of　data　and　the　dependence　on　human-designed　features　in　existing　Chinese　microblog　sentiment　classification　methods,　a　Chinese　microblog　sentiment　classification　method　based　on　convolutional　neural　network　is　proposed.　Firstly,　microblog　messages　are　extended　using　the　interaction　context,　and　then　they　are　initialized　with　dense　vectors　in　the　low-dimension　space.　Secondly,　a　convolutional　neural　network　model　is　constructed　for　extracting　and　combining　features.　Finally,　the　sentiment　of　each　microblog　message　is　estimated　by　softmax　function.　Experimental　results　show　that　compared　with　baselines,　the　proposed　method　obtains　higher　accuracies　and　F1　values.　©　2016,　Science　Press.　All　right　reserved.