  Opinion and sentiment analysis is a vital task to characterize subjective
information in social media posts. In this paper, we present a comprehensive
experimental evaluation and comparison with six state-of-the-art methods, from
which we have re-implemented one of them. In addition, we investigate different
textual and visual feature embeddings that cover different aspects of the
content, as well as the recently introduced multimodal CLIP embeddings.
Experimental results are presented for two different publicly available
benchmark datasets of tweets and corresponding images. In contrast to the
evaluation methodology of previous work, we introduce a reproducible and fair
evaluation scheme to make results comparable. Finally, we conduct an error
analysis to outline the limitations of the methods and possibilities for the
future work.
