  Multimodal Sentiment Analysis (MSA) has been a popular topic in natural
language processing nowadays, at both sentence and aspect level. However, the
existing approaches almost require large-size labeled datasets, which bring
about large consumption of time and resources. Therefore, it is practical to
explore the method for few-shot sentiment analysis in cross-modalities.
Previous works generally execute on textual modality, using the prompt-based
methods, mainly two types: hand-crafted prompts and learnable prompts. The
existing approach in few-shot multi-modality sentiment analysis task has
utilized both methods, separately. We further design a hybrid pattern that can
combine one or more fixed hand-crafted prompts and learnable prompts and
utilize the attention mechanisms to optimize the prompt encoder. The
experiments on both sentence-level and aspect-level datasets prove that we get
a significant outperformance.
