# Memotion Analysis through the Lens of Joint Embedding

arXiv: https://arxiv.org/abs/2111.07074 · HTML: https://mlsanity.com/paper/2111.07074/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 13 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!](https://mlsanity.com/paper/2008.03781/) (2020)

## Built on by (most cited first, 6 of 6)

- [Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes](https://mlsanity.com/paper/2303.09892/) (2023)
- [Findings of Factify 2: Multimodal Fake News Detection](https://mlsanity.com/paper/2307.10475/) (2023)
- [IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images](https://mlsanity.com/paper/2305.10438/) (2023)
- [SemiMemes: A Semi-supervised Learning Approach for Multimodal Memes Analysis](https://mlsanity.com/paper/2304.00020/) (2023)
- [Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes](https://mlsanity.com/paper/2309.06517/) (2023)
- [DeHate: A Stable Diffusion-based Multimodal Approach to Mitigate Hate Speech in Images](https://mlsanity.com/paper/2509.21787/) (2025)

## Abstract (arXiv)

Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such keys. Meme is typically an image with embedded text onto it. Although, memes are commonly used for fun, they could also be used to spread hate and fake information. That along with its growing ubiquity over several social platforms has caused automatic analysis of memes to become a widespread topic of research. In this paper, we report our initial experiments on Memotion Analysis problem through joint embeddings. Results are marginally yielding SOTA.