Image　emotion　classification　(IEC)　aims　to　extract　the　abstract　emotions　evoked　in　images.　Recently,　language-supervised　methods　such　as　contrastive　language-image　pretraining　(CLIP)　have　demonstrated　superior　performance　in　image　understanding.　However,　the　underexplored　task　of　IEC　presents　three　major　challenges:　a　tremendous　training　objective　gap　between　pretraining　and　IEC,　shared　suboptimal　prompts,　and　invariant　prompts　for　all　instances.　In　this　study,　we　propose　a　general　framework　that　effectively　exploits　the　language-supervised　CLIP　method　for　the　IEC　task.　First,　a　prompt-tuning　method　that　mimics　the　pretraining　objective　of　CLIP　is　introduced,　to　exploit　the　rich　image　and　text　semantics　associated　with　CLIP.　Subsequently,　instance-specific　prompts　are　automatically　composed,　conditioning　them　on　the　categories　and　image　content　of　instances,　diversifying　the　prompts,　and　thus　avoiding　suboptimal　problems.　Evaluations　on　six　widely　used　affective　datasets　show　that　the　proposed　method　significantly　outperforms　state-of-the-art　methods　(up　to　9.29%　accuracy　gain　on　the　EmotionROI　dataset)　on　IEC　tasks　with　only　a　few　trained　parameters.　The　code　is　publicly　available　at　https://github.com/dsn0w/PT-DPC/for　research　purposes.