# Exercise? I thought you said 'Extra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis arXiv: https://arxiv.org/abs/2103.12377 ยท HTML: https://mlsanity.com/paper/2103.12377/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 49 references that are in the map. - First posted on arXiv 23 Mar 2021. ## Builds on (most cited first, 23 of 23) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018) - [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016) - [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017) - [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017) - [Stacked Cross Attention for Image-Text Matching](https://mlsanity.com/paper/1803.08024/) (2018) - [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017) - [Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning](https://mlsanity.com/paper/1612.01887/) (2016) - [Memory Fusion Network for Multi-view Sequential Learning](https://mlsanity.com/paper/1802.00927/) (2018) - [Multi-attention Recurrent Network for Human Communication Comprehension](https://mlsanity.com/paper/1802.00923/) (2018) - [SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!](https://mlsanity.com/paper/2008.03781/) (2020) - [PolyNet: A Pursuit of Structural Diversity in Very Deep Networks](https://mlsanity.com/paper/1611.05725/) (2016) - [Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis](https://mlsanity.com/paper/1905.05812/) (2019) - [IITK at SemEval-2020 Task 8: Unimodal and Bimodal Sentiment Analysis of Internet Memes](https://mlsanity.com/paper/2007.10822/) (2020) - [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) - [NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis](https://mlsanity.com/paper/2011.02788/) (2020) - [Integrating Multimodal Information in Large Pretrained Transformers](https://mlsanity.com/paper/1908.05787/) (2019) ## Built on by (most cited first, 9 of 9) - [MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets](https://mlsanity.com/paper/2109.05184/) (2021) - [Detecting Harmful Memes and Their Targets](https://mlsanity.com/paper/2110.00413/) (2021) - [Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection](https://mlsanity.com/paper/2110.10949/) (2021) - [See, Hear, Read: Leveraging Multimodality with Guided Attention for Abstractive Text Summarization](https://mlsanity.com/paper/2105.09601/) (2021) - [Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities](https://mlsanity.com/paper/2406.07353/) (2024) - [TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore](https://mlsanity.com/paper/2305.17911/) (2023) - [Yes FLoReNce, I Will Do Better Next Time! Agentic Feedback Reasoning for Humorous Meme Detection](https://mlsanity.com/paper/2601.07232/) (2026) - [Meme Sentiment Analysis Enhanced with Multimodal Spatial Encoding and Facial Embedding](https://mlsanity.com/paper/2303.01781/) (2023) - [Unimodal Intermediate Training for Multimodal Meme Sentiment Classification](https://mlsanity.com/paper/2308.00528/) (2023) ## Abstract (arXiv) Today's Internet is awash in memes as they are humorous, satirical, or ironic which make people laugh. According to a survey, 33% of social media users in age bracket [13-35] send memes every day, whereas more than 50% send every week. Some of these memes spread rapidly within a very short time-frame, and their virality depends on the novelty of their (textual and visual) content. A few of them convey positive messages, such as funny or motivational quotes; while others are meant to mock/hurt someone's feelings through sarcastic or offensive messages. Despite the appealing nature of memes and their rapid emergence on social media, effective analysis of memes has not been adequately attempted to the extent it deserves. In this paper, we attempt to solve the same set of tasks suggested in the SemEval'20-Memotion Analysis competition. We propose a multi-hop attention-based deep neural network framework, called MHA-MEME, whose prime objective is to leverage the spatial-domain correspondence between the visual modality (an image) and various textual segments to extract fine-grained feature representations for classification. We evaluate MHA-MEME on the 'Memotion Analysis' dataset for all three sub-tasks - sentiment classification, affect classification, and affect class quantification. Our comparative study shows sota performances of MHA-MEME for all three tasks compared to the top systems that participated in the competition. Unlike all the baselines which perform inconsistently across all three tasks, MHA-MEME outperforms baselines in all the tasks on average. Moreover, we validate the generalization of MHA-MEME on another set of manually annotated test samples and observe it to be consistent. Finally, we establish the interpretability of MHA-MEME.