An　increasing　number　of　reviews　from　the　customers　have　been　available　online.　Thus,　sentiment　classification　for　such　reviews　has　attracted　more　and　more　attention　from　the　natural　language　processing　(NLP)　community.　Related　literature　has　shown　that　sentiment　analysis　can　benefit　from　Deep　Belief　Networks　(DBN).　However,　determining　the　structure　of　the　deep　network　and　improving　its　performance　still　remains　an　open　question.　In　this　paper,　we　propose　a　sophisticated　algorithm　based　on　fuzzy　mathematics　and　genetic　algorithm,　called　evolutionary　fuzzy　deep　belief　networks　with　incremental　rules　(EFDBNI).　We　evaluate　our　proposal　using　empirical　data　sets　that　are　dedicated　for　sentiment　classification.　The　results　show　that　EFDBNI　brings　out　significant　improvement　over　existing　methods.