# NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis

arXiv: https://arxiv.org/abs/2011.02788 · HTML: https://mlsanity.com/paper/2011.02788/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Exercise? I thought you said 'Extra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis](https://mlsanity.com/paper/2103.12377/) (2021)
- [MemeFier: Dual-stage Modality Fusion for Image Meme Classification](https://mlsanity.com/paper/2304.02906/) (2023)
- [NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion Analysis](https://mlsanity.com/paper/2302.08326/) (2023)
- [Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes](https://mlsanity.com/paper/2302.08343/) (2023)

## Abstract (arXiv)

This paper describes our contribution to SemEval 2020 Task 8: Memotion Analysis. Our system learns multi-modal embeddings from text and images in order to classify Internet memes by sentiment. Our model learns text embeddings using BERT and extracts features from images with DenseNet, subsequently combining both features through concatenation. We also compare our results with those produced by DenseNet, ResNet, BERT, and BERT-ResNet. Our results show that image classification models have the potential to help classifying memes, with DenseNet outperforming ResNet. Adding text features is however not always helpful for Memotion Analysis.