Plenty　and　well　labeled　training　samples　are　significant　foundation　to　make　sure　the　good　performance　of　supervising　learning,　whereas　there　is　a　problem　of　high　labor-cost　and　time-consuming　in　the　samples.　Furthermore,　it　is　not　always　feasible　to　get　the　plenty　of　well-labeled　sample　data　in　every　application　to　support　the　classification　training.　Meanwhile,　directly　employing　the　trained　model　from　the　source　domain　to　the　target　domain　normally　causes　the　problem　of　accuracy　degradation,　due　to　the　information　distribution　discrepancy　between　the　source　domain　and　the　target　domain.　Aiming　to　solve　the　above　problems,　we　propose　an　algorithm　named　domain　alignment　based　on　multi-viewpoint　domain-shared　feature　for　cross-domain　sentiment　classification　(DAMF).　Firstly,　we　fuse　three　sentiment　lexicons　to　eliminate　the　polarity　divergence　of　domain-shared　feature　words　that　are　chosen　by　mutual　information　value.　On　this　basis,　we　extract　the　word　pairs　that　have　the　same　sentiment　polarity　in　the　same　domain　by　utilizing　four　syntax　rules　and　the　word　pairs　that　have　strong　association　relation　in　the　same　domain　by　utilizing　association　rules　algorithm.　Then,　we　use　the　domain-shared　words　that　have　no　polarity　divergence　as　a　bridge　to　establish　an　indirect　mapping　relationship　between　domain-specific　words　in　different　domains.　By　constructing　the　unified　feature　representation　space　of　different　domains,　the　domain　alignment　is　achieved.　Meanwhile,　the　experiments　on　four　public　data　sets　from　Amazon　product　reviews　corpora　show　the　effectiveness　of　our　proposed　algorithm　on　cross-domain　sentiment　classification.　©　2018,　Science　Press.　All　right　reserved.