# Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes

arXiv: https://arxiv.org/abs/2309.06517 · HTML: https://mlsanity.com/paper/2309.06517/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 77 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark](https://mlsanity.com/paper/1910.04867/) (2019)
- [Exploring Hate Speech Detection in Multimodal Publications](https://mlsanity.com/paper/1910.03814/) (2019)
- [SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!](https://mlsanity.com/paper/2008.03781/) (2020)
- [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://mlsanity.com/paper/2006.00206/) (2020)
- [SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets](https://mlsanity.com/paper/2008.04277/) (2020)
- [A Sentiment Analysis Dataset for Code-Mixed Malayalam-English](https://mlsanity.com/paper/2006.00210/) (2020)
- [XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection](https://mlsanity.com/paper/2011.01612/) (2020)
- [Multimodal Sentiment Analysis To Explore the Structure of Emotions](https://mlsanity.com/paper/1805.10205/) (2018)
- [Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes](https://mlsanity.com/paper/2303.09892/) (2023)
- [UPB at SemEval-2020 Task 8: Joint Textual and Visual Modeling in a Multi-Task Learning Architecture for Memotion Analysis](https://mlsanity.com/paper/2009.02779/) (2020)
- [Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering](https://mlsanity.com/paper/2110.11592/) (2021)
- [Hater-O-Genius Aggression Classification using Capsule Networks](https://mlsanity.com/paper/2105.11219/) (2021)
- [Understanding Chat Messages for Sticker Recommendation in Messaging Apps](https://mlsanity.com/paper/1902.02704/) (2019)
- [Offense Detection in Dravidian Languages using Code-Mixing Index based Focal Loss](https://mlsanity.com/paper/2111.06916/) (2021)
- [Memotion Analysis through the Lens of Joint Embedding](https://mlsanity.com/paper/2111.07074/) (2021)
- [HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection](https://mlsanity.com/paper/2008.09820/) (2020)
- [The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse](https://mlsanity.com/paper/2111.10390/) (2021)
- [IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images](https://mlsanity.com/paper/2305.10438/) (2023)
- [PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages](https://mlsanity.com/paper/2111.06599/) (2021)
- [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)
- [NYCU-TWO at Memotion 3: Good Foundation, Good Teacher, then you have Good Meme Analysis](https://mlsanity.com/paper/2302.06078/) (2023)

## Built on by (most cited first, 2 of 2)

- [MHS-STMA: Multimodal Hate Speech Detection via Scalable Transformer-Based Multilevel Attention Framework](https://mlsanity.com/paper/2409.05136/) (2024)
- [LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs](https://mlsanity.com/paper/2412.17131/) (2024)

## Abstract (arXiv)

Analyzing memes on the internet has emerged as a crucial endeavor due to the impact this multi-modal form of content wields in shaping online discourse. Memes have become a powerful tool for expressing emotions and sentiments, possibly even spreading hate and misinformation, through humor and sarcasm. In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23. The task released an annotated dataset of Hindi-English code-mixed memes based on their Sentiment (Task A), Emotion (Task B), and Emotion intensity (Task C). Each of these is defined as an individual task and the participants are ranked separately for each task. Over 50 teams registered for the shared task and 5 made final submissions to the test set of the Memotion 3 dataset. CLIP, BERT modifications, ViT etc. were the most popular models among the participants along with approaches such as Student-Teacher model, Fusion, and Ensembling. The best final F1 score for Task A is 34.41, Task B is 79.77 and Task C is 59.82.