@inproceedings{
luo2024panosent,
title={PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis},
author={Meng Luo and Hao Fei and Bobo Li and Shengqiong Wu and Qian Liu and Soujanya Poria and Erik Cambria and Mong-Li Lee and Wynne Hsu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=c045hdQdFX}
}
While existing Aspect-based Sentiment Analysis (ABSA) has received extensive effort and advancement, there are still gaps in  defining a more holistic research target seamlessly integrating multimodality, conversation context, fine-granularity, and also covering the changing sentiment dynamics as well as cognitive causal rationales. This paper bridges the gaps by introducing a multimodal conversational ABSA, where two novel subtasks are proposed: 1) Panoptic Sentiment Sextuple Extraction, panoramically recognizing holder, target, aspect, opinion, sentiment, rationale from multi-turn multi-party multimodal dialogue. 2) Sentiment Flipping Analysis, detecting the dynamic sentiment transformation throughout the conversation with the causal reasons. To benchmark the tasks, we construct PanoSent, a dataset annotated both manually and automatically, featuring high quality, large scale (10,000 dialogues), multimodality (text, image, audio and video), multilingualism (English, Chinese and Spanish), multi-scenarios (over 100 domains), and covering both implicit&explicit sentiment elements. Further, to effectively address the tasks, we devise a novel Chain-of-Sentiment reasoning framework, together with a novel multimodal large language model (namely Sentica) and a paraphrase-based verification mechanism. Extensive evaluations demonstrate the superiority of our methods over strong baselines, validating the efficacy of all our proposed methods. The work is expected to open up a new era for the ABSA community, and thus all our codes and data are open at https://PanoSent.github.io/.
PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis
Meng Luo Hao Fei Bobo Li Shengqiong Wu Qian Liu Soujanya Poria Erik Cambria Mong-Li Lee Wynne Hsu