# Multimodal Feature Extraction for Memes Sentiment Classification

arXiv: https://arxiv.org/abs/2207.03317 · HTML: https://mlsanity.com/paper/2207.03317/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Multimodal Residual Learning for Visual QA](https://mlsanity.com/paper/1606.01455/) (2016)
- [Multimodal Machine Translation through Visuals and Speech](https://mlsanity.com/paper/1911.12798/) (2019)
- [The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling](https://mlsanity.com/paper/1403.1452/) (2014)
- [IITK at SemEval-2020 Task 8: Unimodal and Bimodal Sentiment Analysis of Internet Memes](https://mlsanity.com/paper/2007.10822/) (2020)
- [Large Margin Multi-modal Multi-task Feature Extraction for Image Classification](https://mlsanity.com/paper/1904.04088/) (2019)
- [DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis](https://mlsanity.com/paper/2008.00825/) (2020)

## Built on by (most cited first, 3 of 3)

- [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)
- [Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing](https://mlsanity.com/paper/2506.07086/) (2025)

## Abstract (arXiv)

In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. In this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme.