  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.
