Sentiment　analysis　aims　to　detect　the　sentiment　polarity　towards　the　massive　opinions　and　reviews　emerging　on　the　internet.　With　the　increasing　of　multimodal　information　on　social　media,　such　as　text,　image,　audio　and　video,　multimodal　sentiment　analysis　has　attracted　more　attention　in　recent　years　and　our　work　focuses　on　the　text　and　image　data.　The　previous　works　usually　ignore　the　semantic　alignment　between　the　text　and　image,　and　cannot　capture　the　interaction　between　them,　which　will　affect　the　correct　judgement　for　the　sentiment　polarity　prediction.　To　resolve　these　problems,　we　propose　a　novel　multimodal　sentiment　analysis　model　LXMERT-MMSA　based　on　cross-modality　attention　mechanism.　The　single-modality　feature　is　encoded　by　multi-layer　Transformer　encoder　to　achieve　the　deep　semantic　information　implied　in　the　text　and　image.　Moreover,　the　cross-modality　attention　mechanism　enables　the　model　to　fuse　the　text　and　image　features　effectively　and　achieve　the　rich　semantic　information　by　the　alignment.　It　improves　the　ability　of　the　model　to　capture　the　semantic　relation　between　text　and　image.　The　evaluation　metrics　of　accuracy　and　F1　score　are　used,　and　the　experimental　results　on　MVSA-multiple　dataset　and　Twitter　dataset　show　that　our　proposed　model　outperforms　the　previous　SOTA　model,　and　the　ablation　experimental　results　further　prove　that　the　model　can　make　well　use　of　multimodal　features.　©　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature　2024.