  The prevalence of memes on social media has created the need to sentiment
analyze their underlying meanings for censoring harmful content. Meme censoring
systems by machine learning raise the need for a semi-supervised learning
solution to take advantage of the large number of unlabeled memes available on
the internet and make the annotation process less challenging. Moreover, the
approach needs to utilize multimodal data as memes' meanings usually come from
both images and texts. This research proposes a multimodal semi-supervised
learning approach that outperforms other multimodal semi-supervised learning
and supervised learning state-of-the-art models on two datasets, the Multimedia
Automatic Misogyny Identification and Hateful Memes dataset. Building on the
insights gained from Contrastive Language-Image Pre-training, which is an
effective multimodal learning technique, this research introduces SemiMemes, a
novel training method that combines auto-encoder and classification task to
make use of the resourceful unlabeled data.
