mlsanityData to 14 Oct 2026

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

arXiv 2309.06517 ↗First posted 12 Sep 2023cs.CLcited by 2

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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.

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

30 of 77 references.

References · 30

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18 more
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Citing papers per year: 2024: 2.

2 papers.

  1. 2024MHS-STMA: Multimodal Hate Speech Detection via Scalable Transformer-Based Multilevel Attention Frameworkcited by 2
  2. 2024LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMscited by 2