<p>Metaphorical expressions evoke stronger emotions than literal language. However, their implicit sentiment mapping characteristics pose significant challenges for sentiment analysis. Existing studies exhibit two major limitations: first, most models focus on modeling the partial of three core elements (the source-domain word <i>S</i>, the target-domain word <i>T</i>, and the metaphorical context <i>C</i>), neglecting the interactions among them in metaphors; second, current research in computational metaphor predominantly adopts a single-type metaphor pattern, making it difficult for models to capture shared representations across different metaphor types, thereby limiting their generalization. To address these issues, this study proposes a multi-type metaphor sentiment analysis framework that jointly models <i>S</i>, <i>T</i>, and <i>C</i>, aiming to accurately identify the sentiment of complex metaphorical texts while revealing the path of metaphorical sentiment transfer, thereby facilitating sentiment analysis of metaphor-rich texts. To effectively model the three elements, a metaphor sentiment analysis model integrating Multi-Task Contrastive Learning and Instruction Tuning (MTCL-IT) is proposed. Finally, multi-perspective experiments are conducted on the constructed English dataset EMSA and Chinese dataset CMSA, both containing <i>S</i>, <i>T</i>, sentiment polarity, and metaphor type information, fully validating the effectiveness of the proposed three-element joint modeling framework and model in sentiment identification and understanding of sentiment mapping mechanisms.</p>