Automatic　classification　of　sentiment　data　(e.g.,　reviews,　blogs)　has　many　applications　in　enterprise　user　management　systems,　and　can　help　us　understand　people＇s　attitudes　about　products　or　services.　However,　it　is　difficult　to　train　an　accurate　sentiment　classifier　for　different　domains.　One　of　the　major　reasons　is　that　people　often　use　different　words　to　express　the　same　sentiment　in　different　domains,　and　we　cannot　easily　find　a　direct　mapping　relationship　between　them　to　reduce　the　differences　between　domains.　So,　the　accuracy　of　the　sentiment　classifier　will　decline　sharply　when　we　apply　a　classifier　trained　in　one　domain　to　other　domains.　In　this　paper,　we　propose　a　novel　approach　called　words　alignment　based　on　association　rules　(WAAR)　for　cross-domain　sentiment　classification,　which　can　establish　an　indirect　mapping　relationship　between　domain-specific　words　in　different　domains　by　learning　the　strong　association　rules　between　domain-shared　words　and　domain-specific　words　in　the　same　domain.　In　this　way,　the　differences　between　the　source　domain　and　target　domain　can　be　reduced　to　some　extent,　and　a　more　accurate　cross-domain　classifier　can　be　trained.　Experimental　results　on　Amazon　(R)　datasets　show　the　effectiveness　of　our　approach　on　improving　the　performance　of　cross-domain　sentiment　classification.