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Today's Internet is awash in memes as they are humorous, satirical, or ironicwhich make people laugh. According to a survey, 33% of social media users inage bracket [13-35] send memes every day, whereas more than 50% send everyweek. Some of these memes spread rapidly within a very short time-frame, andtheir virality depends on the novelty of their (textual and visual) content. Afew of them convey positive messages, such as funny or motivational quotes;while others are meant to mock/hurt someone's feelings through sarcastic oroffensive messages. Despite the appealing nature of memes and their rapidemergence on social media, effective analysis of memes has not been adequatelyattempted to the extent it deserves.  In this paper, we attempt to solve the same set of tasks suggested in theSemEval'20-Memotion Analysis competition. We propose a multi-hopattention-based deep neural network framework, called MHA-MEME, whose primeobjective is to leverage the spatial-domain correspondence between the visualmodality (an image) and various textual segments to extract fine-grainedfeature 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 studyshows sota performances of MHA-MEME for all three tasks compared to the topsystems that participated in the competition. Unlike all the baselines whichperform inconsistently across all three tasks, MHA-MEME outperforms baselinesin all the tasks on average. Moreover, we validate the generalization ofMHA-MEME on another set of manually annotated test samples and observe it to beconsistent. Finally, we establish the interpretability of MHA-MEME.

Exercise? I thought you said 'Extra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis