Meme Sentiment Analysis Enhanced with Multimodal Spatial Encoding and Facial Embedding
This work addresses sentiment analysis for internet memes, which is an incremental improvement over existing multimodal classifiers by adding spatial encoding.
The paper tackled the problem of multimodal meme sentiment analysis by incorporating spatial positions of visual objects, faces, and text clusters, along with facial embeddings, resulting in performance gains that outperformed baselines relying on human-validated OCR text.
Internet memes are characterised by the interspersing of text amongst visual elements. State-of-the-art multimodal meme classifiers do not account for the relative positions of these elements across the two modalities, despite the latent meaning associated with where text and visual elements are placed. Against two meme sentiment classification datasets, we systematically show performance gains from incorporating the spatial position of visual objects, faces, and text clusters extracted from memes. In addition, we also present facial embedding as an impactful enhancement to image representation in a multimodal meme classifier. Finally, we show that incorporating this spatial information allows our fully automated approaches to outperform their corresponding baselines that rely on additional human validation of OCR-extracted text.