Sentiment　classification　can　be　widely　used　in　various　fields　because　of　the　value　it　has　gained,　and　has　received　extensive　attention　in　recent　years.　No　matter　the　traditional　methods　based　on　language　rules,　machine　statistical　learning,　or　the　current　popular　deep　learning　have　achieved　great　success　in　this　respect,　but　up　to　now,　because　of　the　particularity　of　Chinese,　there　are　still　few　methods　for　Chinese　text　sentiment　classification.　Moreover,　the　traditional　method　is　time-consuming　and　labor-consuming,　with　poor　versatility.　The　neural　network　method　saves　resources　and　is　generally　applicable,　but　the　application　of　language　knowledge　is　not　sufficient.　For　this　reason,　we　propose　a　method　combining　traditional　method　with　neural　network,　which　combines　sentiment　lexicon,　attention　mechanism　and　stacked　BILSTM.　Word　vectors　are　generated　using　word2vec,　and　the　context　semantics　are　captured　multiple　times　by　stacked　BILSTM　to　get　richer　semantic　information,　for　sentiment　classification,　sentiment　words　often　have　more　influence　on　the　result　of　classification,　so　we　use　sentiment　lexicon　which　contains　rich　sentiment　word　to　monitor　the　attention　mechanism,　and　make　the　attention　model　pay　more　attention　to　sentiment　words.　Combining　the　advantages　of　traditional　methods　and　neural　networks,　we　use　the　proposed　method　to　do　the　sentiment　binary　classification　experiments　on　two　data　sets.　The　experimental　results　show　that　our　proposed　method　is　superior　to　the　previous　method.　©　Published　under　licence　by　IOP　Publishing　Ltd.