Image　emotion　classification　is　an　important　computer　vision　task　to　extract　emotions　from　images.　The　methods　for　image　emotion　classification　(IEC)　are　primarily　based　on　label　or　distribution　as　a　supervision　signal,　which　neither　has　enough　accessibility　nor　diversity,　limiting　the　development　of　IEC　research.　Inspired　by　psychology　research　and　the　recent　booming　of　large-scale　pretrained　language　models.　We　figure　out　a　language-supervised　paradigm,　which　can　cleverly　combine　the　features　of　language　and　visual　emotion　to　drive　the　visual　model　to　gain　stronger　emotional　discernment　with　language　prompts.　To　practice　the　paradigm,　we　present　a　conceptually　simple　while　empirically　powerful　framework　for　image　emotion　classification,　SimEmotion.　That　we　propose　a　prompt-based　fine-tuning　strategy　to　learn　task-specific　representations　by　composing　a　template　with　the　emotion-level　concept　and　entity-level　information.　Evaluations　on　four　widely-used　affective　datasets,　namely,　Flickr　and　Instagram　(FI),　EmotionROI,　Twitter　I,　and　Twitter　II,　demonstrate　that　the　proposed　algorithm　outperforms　the　state-of-the-art　methods　with　a　large　margin　(i.e.,　8.42%　absolute　accuracy　gain　on　EmotionROI)　on　image　emotion　classification　tasks.　Our　codes　will　be　publicly　available　for　research　purposes.