  Information on social media comprises of various modalities such as textual,
visual and audio. NLP and Computer Vision communities often leverage only one
prominent modality in isolation to study social media. However, the
computational processing of Internet memes needs a hybrid approach. The growing
ubiquity of Internet memes on social media platforms such as Facebook,
Instagram, and Twiter further suggests that we can not ignore such multimodal
content anymore. To the best of our knowledge, there is not much attention
towards meme emotion analysis. The objective of this proposal is to bring the
attention of the research community towards the automatic processing of
Internet memes. The task Memotion analysis released approx 10K annotated memes,
with human-annotated labels namely sentiment (positive, negative, neutral),
type of emotion (sarcastic, funny, offensive, motivation) and their
corresponding intensity. The challenge consisted of three subtasks: sentiment
(positive, negative, and neutral) analysis of memes, overall emotion (humour,
sarcasm, offensive, and motivational) classification of memes, and classifying
intensity of meme emotion. The best performances achieved were F1 (macro
average) scores of 0.35, 0.51 and 0.32, respectively for each of the three
subtasks.
