Image　sentiment　analysis　is　a　domain　fraught　with　the　dual　challenges　of　interpreting　complex　visual　content　and　discerning　the　subtle　emotional　undertones　it　may　convey.　Despite　the　notable　successes　of　existing　visual　language　pretraining　(VLP)　models　in　a　variety　of　visual　tasks,　they　fall　short　in　the　nuanced　realm　of　sentiment　analysis.　This　shortfall　is　primarily　due　to　their　inadequate　processing　of　sentiment-specific　cues　-　most　notably,　the　oversight　of　localized　sentioment　cues　within　images　and　the　intricate　interplay　of　these　signals.　Furthermore,　these　models　inadequately　harness　the　rich　sentiment　cues　often　embedded　in　accompanying　text.　In　response　to　these　shortcomings,　we　introduce　the　Specific　Sentiment　Mask　Auto-encoder　(S2MA)　model,　which　is　expressly　designed　to　integrate　sentiment　information　during　the　pretraining　process.　S2MA　is　meticulously　engineered　to　focuse　on　both　intermodal　and　intramodal　sentiment　cue,　thereby　augmenting　the　model＇s　proficiency　in　anlysising　the　sentiment　knowledge　within　visual　content.　Rigorous　comparative　evaluations　of　S2MA　against　the　CLIP　model,　across　a　spectrum　of　downstream　datasets　in　zero-shot　and　supervised　learning　scenarios,　have　validated　the　superiority　of　our　approach.　The　empirical　outcomes　affirm　S2MA＇s　capacity　to　significantly　enhance　the　analytical　landscape　of　image　sentiment　analysis.　©　2024　IEEE.