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Chimera: A cognitive and aesthetic sentiment causality understanding framework

This repository contains the source code and datasets associated with the paper titled "Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysis."

Overview

Requirements

  • conda env create -f Chimera.yaml

Datasets

  1. Constructed datasets: Twitter2015 (twitter2015), Twitter2017 (twitter2017) and Political Twitter (political_twitter).
  2. Image features can be downloaded from Google Drive. Place the downloaded files in the directories data/twitter2015 and data/twitter2017, respectively. For the political_twitter dataset, move the contents of data/twitter2015 and data/twitter2017 into data/political_twitter and extract the two .zip files into the same directory.

Pretrained Models

  • The Flan-T5 model is utilized as the backbone. Download the pre-trained model google/flan-t5-base and save it in the directory pretrained/flan-t5-base.

Training and Evaluating

python run_chimera_15.py

python run_chimera_17.py

python run_chimera_political.py

Reference

If you find this repository beneficial, we kindly encourage you to cite our related papers and consider starring the repository.

@article{xiao2025exploring,
  title={Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysis},
  author={Xiao, Luwei and Mao, Rui and Zhao, Shuai and Lin, Qika and Jia, Yanhao and He, Liang and Cambria, Erik},
  journal={arXiv preprint arXiv:2504.15848},
  year={2025}
}

@article{xiao2024atlantis,
  title={Atlantis: Aesthetic-oriented multiple granularities fusion network for joint multimodal aspect-based sentiment analysis},
  author={Xiao, Luwei and Wu, Xingjiao and Xu, Junjie and Li, Weijie and Jin, Cheng and He, Liang},
  journal={Information Fusion},
  volume={106},
  pages={102304},
  year={2024},
  publisher={Elsevier}
}

@inproceedings{xiao2024vanessa,
  title={Vanessa: Visual connotation and aesthetic attributes understanding network for multimodal aspect-based sentiment analysis},
  author={Xiao, Luwei and Mao, Rui and Zhang, Xulang and He, Liang and Cambria, Erik},
  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2024},
  pages={11486--11500},
  year={2024}
}

Acknoeledgement

This work is primarily built upon the repositories of MDCA and LAPS. Sincere gratitude is extended to everyone who contributed to this project for their invaluable support and dedication.