  Multimodal aspect-based sentiment analysis (MABSA) aims to understand
opinions in a granular manner, advancing human-computer interaction and other
fields. Traditionally, MABSA methods use a joint prediction approach to
identify aspects and sentiments simultaneously. However, we argue that joint
models are not always superior. Our analysis shows that joint models struggle
to align relevant text tokens with image patches, leading to misalignment and
ineffective image utilization.
  In contrast, a pipeline framework first identifies aspects through MATE
(Multimodal Aspect Term Extraction) and then aligns these aspects with image
patches for sentiment classification (MASC: Multimodal Aspect-Oriented
Sentiment Classification). This method is better suited for multimodal
scenarios where effective image use is crucial. We present three key
observations: (a) MATE and MASC have different feature requirements, with MATE
focusing on token-level features and MASC on sequence-level features; (b) the
aspect identified by MATE is crucial for effective image utilization; and (c)
images play a trivial role in previous MABSA methods due to high noise.
  Based on these observations, we propose a pipeline framework that first
predicts the aspect and then uses translation-based alignment (TBA) to enhance
multimodal semantic consistency for better image utilization. Our method
achieves state-of-the-art (SOTA) performance on widely used MABSA datasets
Twitter-15 and Twitter-17. This demonstrates the effectiveness of the pipeline
approach and its potential to provide valuable insights for future MABSA
research.
  For reproducibility, the code and checkpoint will be released.
