  Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has
gained significant attention among scholars. However, current multimodal models
have reached a performance bottleneck. To investigate the causes of this
problem, we perform extensive empirical evaluation and in-depth analysis of the
datasets to answer the following questions: Q1: Are the modalities equally
important for TMSC? Q2: Which multimodal fusion modules are more effective? Q3:
Do existing datasets adequately support the research? Our experiments and
analyses reveal that the current TMSC systems primarily rely on the textual
modality, as most of targets' sentiments can be determined solely by text.
Consequently, we point out several directions to work on for the TMSC task in
terms of model design and dataset construction. The code and data can be found
in https://github.com/Junjie-Ye/RethinkingTMSC.
