mlsanityData to 13 Oct 2026

Multimodal Analysis of memes for sentiment extraction

arXiv 2112.11850 ↗First posted 22 Dec 2021cs.CL cs.AI cs.CVcited by 5

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Abstract (arXiv)

Memes are one of the most ubiquitous forms of social media communication. The study and processing of memes, which are intrinsically multimedia, is a popular topic right now. The study presented in this research is based on the Memotion dataset, which involves categorising memes based on irony, comedy, motivation, and overall-sentiment. Three separate innovative transformer-based techniques have been developed, and their outcomes have been thoroughly reviewed.The best algorithm achieved a macro F1 score of 0.633 for humour classification, 0.55 for motivation classification, 0.61 for sarcasm classification, and 0.575 for overall sentiment of the meme out of all our techniques.

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 22 Dec 2021.

Builds on

9 of 14 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,322
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,107
  5. 2018MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversationscited by 637
  6. 2019SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimizationcited by 253
  7. 2017Deep Learning for Hate Speech Detection in Tweetscited by 180
  8. 2020SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!cited by 77
  9. 2018Dank Learning: Generating Memes Using Deep Neural Networkscited by 10

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.