Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes
Abstract
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.
Keywords
Cite
@article{arxiv.2309.06517,
title = {Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes},
author = {Shreyash Mishra and S Suryavardan and Megha Chakraborty and Parth Patwa and Anku Rani and Aman Chadha and Aishwarya Reganti and Amitava Das and Amit Sheth and Manoj Chinnakotla and Asif Ekbal and Srijan Kumar},
journal= {arXiv preprint arXiv:2309.06517},
year = {2023}
}
Comments
Defactify2 @AAAI 2023