# IITK at SemEval-2020 Task 8: Unimodal and Bimodal Sentiment Analysis of Internet Memes

arXiv: https://arxiv.org/abs/2007.10822 · HTML: https://mlsanity.com/paper/2007.10822/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Exercise? I thought you said 'Extra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis](https://mlsanity.com/paper/2103.12377/) (2021)
- [MemeFier: Dual-stage Modality Fusion for Image Meme Classification](https://mlsanity.com/paper/2304.02906/) (2023)
- [BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer](https://mlsanity.com/paper/2202.07543/) (2022)
- [Multimodal Feature Extraction for Memes Sentiment Classification](https://mlsanity.com/paper/2207.03317/) (2022)
- [Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph](https://mlsanity.com/paper/2208.09417/) (2022)
- [Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis](https://mlsanity.com/paper/2209.14667/) (2022)
- [Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing](https://mlsanity.com/paper/2506.07086/) (2025)
- [Unimodal Intermediate Training for Multimodal Meme Sentiment Classification](https://mlsanity.com/paper/2308.00528/) (2023)
- [Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes](https://mlsanity.com/paper/2302.08343/) (2023)
- [IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes](https://mlsanity.com/paper/2404.04520/) (2024)
- [IITK at SemEval-2024 Task 10: Who is the speaker? Improving Emotion Recognition and Flip Reasoning in Conversations via Speaker Embeddings](https://mlsanity.com/paper/2404.04525/) (2024)
- [Enhancing Sentiment Analysis through Multimodal Fusion: A BERT-DINOv2 Approach](https://mlsanity.com/paper/2503.07943/) (2025)

## Abstract (arXiv)

Social media is abundant in visual and textual information presented together or in isolation. Memes are the most popular form, belonging to the former class. In this paper, we present our approaches for the Memotion Analysis problem as posed in SemEval-2020 Task 8. The goal of this task is to classify memes based on their emotional content and sentiment. We leverage techniques from Natural Language Processing (NLP) and Computer Vision (CV) towards the sentiment classification of internet memes (Subtask A). We consider Bimodal (text and image) as well as Unimodal (text-only) techniques in our study ranging from the Na"ive Bayes classifier to Transformer-based approaches. Our results show that a text-only approach, a simple Feed Forward Neural Network (FFNN) with Word2vec embeddings as input, performs superior to all the others. We stand first in the Sentiment analysis task with a relative improvement of 63% over the baseline macro-F1 score. Our work is relevant to any task concerned with the combination of different modalities.