Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.22603 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912791301980160 |
|---|---|
| author | Gao, Zhiqiang Gao, Shihao Zhang, Zixing Guo, Yihao Chen, Hongyu Han, Jing |
| author_facet | Gao, Zhiqiang Gao, Shihao Zhang, Zixing Guo, Yihao Chen, Hongyu Han, Jing |
| contents | Understanding sentiment in multimodal conversations is a complex yet crucial challenge toward building emotionally intelligent AI systems. The Multimodal Conversational Aspect-based Sentiment Analysis (MCABSA) Challenge invited participants to tackle two demanding subtasks: (1) extracting a comprehensive sentiment sextuple, including holder, target, aspect, opinion, sentiment, and rationale from multi-speaker dialogues, and (2) detecting sentiment flipping, which detects dynamic sentiment shifts and their underlying triggers. For Subtask-I, in the present paper, we designed a structured prompting pipeline that guided large language models (LLMs) to sequentially extract sentiment components with refined contextual understanding. For Subtask-II, we further leveraged the complementary strengths of three LLMs through ensembling to robustly identify sentiment transitions and their triggers. Our system achieved a 47.38% average score on Subtask-I and a 74.12% exact match F1 on Subtask-II, showing the effectiveness of step-wise refinement and ensemble strategies in rich, multimodal sentiment analysis tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22603 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structured Prompting and LLM Ensembling for Multimodal Conversational Aspect-based Sentiment Analysis Gao, Zhiqiang Gao, Shihao Zhang, Zixing Guo, Yihao Chen, Hongyu Han, Jing Computation and Language Understanding sentiment in multimodal conversations is a complex yet crucial challenge toward building emotionally intelligent AI systems. The Multimodal Conversational Aspect-based Sentiment Analysis (MCABSA) Challenge invited participants to tackle two demanding subtasks: (1) extracting a comprehensive sentiment sextuple, including holder, target, aspect, opinion, sentiment, and rationale from multi-speaker dialogues, and (2) detecting sentiment flipping, which detects dynamic sentiment shifts and their underlying triggers. For Subtask-I, in the present paper, we designed a structured prompting pipeline that guided large language models (LLMs) to sequentially extract sentiment components with refined contextual understanding. For Subtask-II, we further leveraged the complementary strengths of three LLMs through ensembling to robustly identify sentiment transitions and their triggers. Our system achieved a 47.38% average score on Subtask-I and a 74.12% exact match F1 on Subtask-II, showing the effectiveness of step-wise refinement and ensemble strategies in rich, multimodal sentiment analysis tasks. |
| title | Structured Prompting and LLM Ensembling for Multimodal Conversational Aspect-based Sentiment Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.22603 |