Aspect-Oriented Semantic Enhancement Network For Aspect-Level Multimodal Sentiment Analysis

As a fine-grained sentiment analysis task, Aspect-Level Multimodal Sentiment Analysis (AMSA) aims to identify the sentiment polarity of each aspect term within given text-image pairs. Existing methods simply utilize attention mechanisms to adaptively search for the associated sentiment between aspects in a sentence, overlooking the fact that sentiment judgments can be easily interfered with by other irrelevant words. Additionally, blindly leveraging image information may result in coarse details, which fail to accurately capture aspect-specific information or overlook global sentiment trends. To address these challenges, we propose a novel Aspect-Oriented Semantic Enhancement Network (ASEN) for aspect-level multimodal sentiment analysis. Specifically, our model contains an aspect-aware enhancement module that is sensitive to aspect-related semantic information based on syntactic structure and part-of-speech information. Furthermore, we introduce an aspect-oriented image sentiment module that precisely captures sentiment-relevant visual cues corresponding to different aspect terms through an adjective mapping method. To capture the overall sentiment trend, we employ a global sentiment perception module that provides auxiliary sentiment information to enhance aspect-based sentiment analysis. Experiment results show that our method achieves state-of-the-art results on the Twitter-2015 and Twitter-2017 datasets.