With　the　development　of　internet,　more　and　more　people　share　reviews.　Efficient　sentiment　analysis　over　such　reviews　using　deep　learning　techniques　has　become　an　emerging　research　topic,　which　has　attracted　more　and　more　attention　from　the　natural　language　processing　community.　However,　improving　performance　of　a　deep　neural　network　remains　an　open　question.　In　this　paper,　we　propose　a　sophisticated　algorithm　based　on　deep　learning,　fuzzy　clustering　and　information　geometry.　In　particular,　the　distribution　of　training　samples　is　treated　as　prior　knowledge　and　is　encoded　in　fuzzy　deep　belief　networks　using　an　improved　Fuzzy　C-Means　(FCM)　clustering　algorithm.　We　adopt　information　geometry　to　construct　geodesic　distance　between　the　distributions　over　features　for　classification,　improving　the　FCM.　Based　on　the　clustering　results,　we　then　embed　the　fuzzy　rules　learned　by　FCM　into　fuzzy　deep　belief　networks　in　order　to　improve　their　performance.　Finally,　we　evaluate　our　proposal　using　empirical　data　sets　that　are　dedicated　for　sentiment　classification.　The　results　show　that　our　algorithm　brings　out　significant　improvement　over　existing　methods.