PSA-MF: Personality-Sentiment Aligned Multi-Level Fusion for Multimodal Sentiment Analysis
Heng Xie, Kang Zhu, Zhengqi Wen, Jianhua Tao, Xuefei Liu, Ruibo Fu, Changsheng Li
Abstract
Multimodal sentiment analysis (MSA) is a research field that recognizes human sentiments by combining textual, visual, and audio modalities. The main challenge lies in integrating sentiment-related information from different modalities, which typically arises during the unimodal feature extraction phase and the multimodal feature fusion phase. Existing methods extract only shallow information from unimodal features during the extraction phase, neglecting sentimental differences across different personalities. During the fusion phase, they directly merge the feature information from each modality without considering differences at the feature level. This ultimately affects the model
BibTeX
@inproceedings{aaai2026_psamfpersonality,
title = {PSA-MF: Personality-Sentiment Aligned Multi-Level Fusion for Multimodal Sentiment Analysis},
author = {Heng Xie and Kang Zhu and Zhengqi Wen and Jianhua Tao and Xuefei Liu and Ruibo Fu and Changsheng Li},
booktitle = {AAAI 2026},
year = {2026}
}