  This paper investigates the effectiveness and implementation of
modality-specific large-scale pre-trained encoders for multimodal sentiment
analysis~(MSA). Although the effectiveness of pre-trained encoders in various
fields has been reported, conventional MSA methods employ them for only
linguistic modality, and their application has not been investigated. This
paper compares the features yielded by large-scale pre-trained encoders with
conventional heuristic features. One each of the largest pre-trained encoders
publicly available for each modality are used; CLIP-ViT, WavLM, and BERT for
visual, acoustic, and linguistic modalities, respectively. Experiments on two
datasets reveal that methods with domain-specific pre-trained encoders attain
better performance than those with conventional features in both unimodal and
multimodal scenarios. We also find it better to use the outputs of the
intermediate layers of the encoders than those of the output layer. The codes
are available at https://github.com/ando-hub/MSA_Pretrain.

Computation and Language;Sound;Audio and Speech Processing