Sentiment　analysis,　including　aspect-level　sentiment　classification,　is　an　important　basic　natural　language　processing　(NLP)　task.　Aspect-level　sentiment　can　provide　complete　and　in-depth　results.　Words　with　different　contexts　variably　influence　the　aspect-level　sentiment　polarity　of　sentences,　and　polarity　varies　based　on　different　aspects　of　a　sentence.　Recurrent　neural　networks　(RNNs)　are　regarded　as　effective　models　for　handling　NLP　and　have　performed　well　in　aspect-level　sentiment　classification.　Extensive　literature　exists　on　sentiment　classification　that　utilizes　convolutional　neural　networks　(CNNs);　however,　no　literature　on　aspect-level　sentiment　classification　that　uses　CNNs　is　available.　In　the　present　study,　we　develop　a　CNN　model　for　handling　aspect-level　sentiment　classification.　In　our　model,　attention-based　input　layers　are　incorporated　into　CNN　to　introduce　aspect　information.　In　our　experiment,　in　which　a　benchmark　dataset　from　Twitter　is　compared　with　other　models,　incorporating　aspect　information　into　CNN　improves　aspect-level　sentiment　classification　performance　without　using　syntactic　parser　or　other　language　features.