{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:11:10Z","timestamp":1760029870792,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T00:00:00Z","timestamp":1742860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Higher Education Stability Support Program Project","award":["GXWD20231130133530002"],"award-info":[{"award-number":["GXWD20231130133530002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the rapid advancement of human\u2013machine dialogue technology, sentiment analysis has become increasingly crucial. However, deep learning-based methods struggle with interpretability and reliability due to the subjectivity of emotions and the challenge of capturing emotion\u2013cause relationships. To address these issues, we propose a novel sentiment analysis framework that integrates structured commonsense knowledge to explicitly infer emotional causes, enabling causal reasoning between historical and target sentences. Additionally, we enhance sentiment classification by leveraging large language models (LLMs) with dynamic example retrieval, constructing an experience database to guide the model using contextually relevant instances. To further improve adaptability, we design a semantic interpretation task for refining emotion category representations and fine-tune the LLM accordingly. Experiments on three benchmark datasets show that our approach significantly improves accuracy and reliability, surpassing traditional deep-learning methods. These findings underscore the effectiveness of structured reasoning, knowledge retrieval, and LLM-driven sentiment adaptation in advancing emotion\u2013cause-based sentiment analysis.<\/jats:p>","DOI":"10.3390\/sym17040489","type":"journal-article","created":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T06:25:15Z","timestamp":1742883915000},"page":"489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["CDEA: Causality-Driven Dialogue Emotion Analysis via LLM"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8169-4453","authenticated-orcid":false,"given":"Xue","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory for Key Technologies of IoT Terminals, Harbin Institute of Technology, Shenzhen 518055, China"},{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China"}]},{"given":"Mingjiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Key Technologies of IoT Terminals, Harbin Institute of Technology, Shenzhen 518055, China"},{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China"}]},{"given":"Xuyi","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Key Technologies of IoT Terminals, Harbin Institute of Technology, Shenzhen 518055, China"},{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China"}]},{"given":"Xiao","family":"Zeng","sequence":"additional","affiliation":[{"name":"Key Laboratory for Key Technologies of IoT Terminals, Harbin Institute of Technology, Shenzhen 518055, China"},{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Zhili Middle School, Shenzhen 518055, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,25]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Zhou, H., Huang, M., Zhang, T., Zhu, X., and Liu, B. 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