Sentiment　classification　can　explore　the　opinions　expressed　by　people　and　help　them　make　better　decisions.　With　the　increasing　of　multimodal　contents　on　the　web,　such　as　text,　image,　audio　and　video,　how　to　make　full　use　of　them　is　important　in　many　tasks,　including　sentiment　classification.　This　paper　focuses　on　the　text　and　image.　Previous　work　cannot　capture　the　fine-grained　features　of　images,　and　those　models　bring　a　lot　of　noise　during　feature　fusion.　In　this　work,　we　propose　a　novel　multimodal　sentiment　classification　model　based　on　gated　attention　mechanism.　The　image　feature　is　used　to　emphasize　the　text　segment　by　the　attention　mechanism　and　it　allows　the　model　to　focus　on　the　text　that　affects　the　sentiment　polarity.　Moreover,　the　gating　mechanism　enables　the　model　to　retain　useful　image　information　while　ignoring　the　noise　introduced　during　the　fusion　of　image　and　text.　The　experiment　results　on　Yelp　multimodal　dataset　show　that　our　model　outperforms　the　previous　SOTA　model.　And　the　ablation　experiment　results　further　prove　the　effectiveness　of　different　strategies　in　the　proposed　model.　(C)　2022　Elsevier　B.V.　All　rights　reserved.