Automatic　sentiment　analysis　of　social　media　texts　is　of　great　significance　for　identifying　people＇s　opinions　that　can　help　people　make　better　decisions.　Annotating　data　is　time　consuming　and　laborious,　and　effective　sentiment　analysis　on　domains　lacking　of　labeled　data　has　become　a　problem.　Crossdomain　sentiment　classification　is　a　promising　task,　which　leverages　the　source　domain　data　with　rich　sentiment　labels　to　analyze　the　sentiment　polarity　of　the　target　domain　lacking　supervised　information.　Most　of　the　existing　researches　usually　explore　algorithms　that　select　common　features　manually　to　bridge　different　domains.　In　this　paper,　we　propose　a　Wasserstein　based　Transfer　Network　(WTN)　to　share　the　domain-invariant　information　of　source　and　target　domains.　We　benefit　from　BERT　to　achieve　rich　knowledge　and　obtain　deep　level　semantic　information　of　text.　The　recurrent　neural　network　with　attention　is　used　to　capture　features　automatically,　and　Wasserstein　distance　is　applied　to　estimate　feature　representations　of　source　and　target　domains,　which　could　help　to　capture　significant　domain-invariant　features　by　adversarial　training.　Extensive　experiments　on　Amazon　datasets　demonstrate　that　WTN　outperforms　other　state-of-the-art　methods　significantly.　Especially,　the　model　behaves　more　stable　across　different　domains.　(C)　2020　Elsevier　B.V.　All　rights　reserved.