Local Feature Alignment Prompt-Tuning for Few-shot Multimodal Aspect Sentiment Analysis

Abstract

Multi-modal aspect-oriented sentiment classification (MASC) is a fine-grain task, which aims to detect the sentiment polarity of specific aspect. However, conventional studies suffer from two issues. It is difficult to collect the annotated multi-modal data in fine-grained domains. Meanwhile, the local information corresponding to aspect words in the image has not been mined, and redundant information can affect the accuracy of fine-grained sentiment analysis. To alleviate the above two issues, we propose a Prompt-tuning method based on Alignment between Aspect and Local Images (PAALI). Our approach introduces a novel multi-modal prompt template to bridge the gap between text and visual data modalities. Furthermore, we employ a strategy of randomly masking image patches to align them with aspect word features, capturing deeper semantic information. Extensive experiments on multiple benchmark datasets in few-shot settings consistently demonstrate the superiority and robustness of our PAALI method over state-of-the-art competitors.