Abstract: Currently, aspect-based multimodal sentiment analysis faces challenges such as the scarcity of Chinese datasets and uneven distribution of categories in related tasks. Traditional models often ignore the local dependencies of words when processing sentiment information, which leads to insufficient global semantic understanding and makes it difficult to accurately localize the sentiment information. In addition, it is challenging to effectively screen and filter irrelevant information during multimodal information fusion, which affects the accuracy of sentiment classification. To solve these problems, this paper constructs a high-quality multimodal Chinese dataset named WAMSA and proposes an aspect-based multimodal sentiment analysis model based on panoramic semantics and multi-level feature fusion (PSMFF). This model employs a panoramic semantic network module to integrate textual features with semantic expansion information, utilizing GCN and graph encoders to capture fine-grained and coarse-grained semantic features. The multi-level feature fusion module extracts relevant image features through local guidance, enhances them via  a Transformer, and subsequently fuses them with textual features through global guidance to generate rich multimodal representations. Experimental results demonstrate that the PSMFF model outperforms multiple baseline models on 3 datasets. 

Key words: aspect-based multimodal sentiment analysis, Weibo aspect-level multimodal sentiment analysis（WAMSA） dataset, panoramic semantic network, multi-level feature fusion