Most　of　existing　cross-domain　sentiment　classification　methods　are　not　expressive　enough　to　capture　rich　representation　of　texts,　and　class　noise　accumulated　during　transfer　process　would　lead　to　negative　transfer　which　could　adversely　affect　performance.　To　address　these　issues,　the　authors　propose　a　method　combining　textual　representation　learning　and　transfer　learning　algorithm　for　cross-domain　sentiment　classification.　This　method　first　builds　a　hierarchical　attention　network　to　generate　document　representations　with　local　semantic　information.　Afterwards,　the　authors　utilize　the　class-noise　estimation　algorithm　to　detect　the　negative　transfer　samples　in　transferred　samples　and　remove　them.　Finally,　the　sentiment　classifier　is　trained　on　the　expanded　dataset　from　samples　in　target　domain　and　transferred　ones　in　source　domain.　Compared　with　the　baselines,　two　experiments　on　large-scale　product　review　datasets　show　that　the　proposed　method　is　able　to　effectively　reduce　RMSE　of　cross-domain　sentiment　classification　by　1.5%　and　1.0%　respectively.　©　2019　Peking　University.