Unimodal Intermediate Training for Multimodal Meme Sentiment Classification
Muzhaffar Hazman, Susan McKeever, Josephine Griffith · 2023
Internet Memes remain a challenging form of user-generated content for automated sentiment classification.The availability of labelled memes is a barrier to developing sentiment classifiers of multimodal memes.To address the shortage of labelled memes, we propose to supplement the training of a multimodal meme classifier with unimodal (image-only and textonly) data.In this work, we present a novel variant of supervised intermediate training that uses relatively abundant sentiment-labelled unimodal data.Our results show a statistically significant performance improvement from the incorporation of unimodal text data.Furthermore, we show that the training set of labelled memes can be reduced by 40% without reducing the performance of the downstream model.