SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!

• URL: http://arxiv.org/abs/2008.03781v1
• Date: Sun, 9 Aug 2020 18:17:33 GMT
• Title: SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!
• Authors: Chhavi Sharma and Deepesh Bhageria and William Scott and Srinivas PYKL and Amitava Das and Tanmoy Chakraborty and Viswanath Pulabaigari and Bjorn Gamback
• Abstract summary: 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 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.
• Score: 20.55903557920223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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