Multimodal Analysis of memes for sentiment extraction

• URL: http://arxiv.org/abs/2112.11850v1
• Date: Wed, 22 Dec 2021 12:57:05 GMT
• Title: Multimodal Analysis of memes for sentiment extraction
• Authors: Nayan Varma Alluri, Neeli Dheeraj Krishna
• Abstract summary: The study is based on the Memotion dataset, which involves categorising memes based on irony, comedy, motivation, and overall-sentiment. The best algorithm achieved a macro F1 score of 0.633 for humour classification, 0.55 for motivation classification, 0.61 for sarcasm classification, and 0.575 for overall sentiment of the meme.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Memes are one of the most ubiquitous forms of social media communication. The study and processing of memes, which are intrinsically multimedia, is a popular topic right now. The study presented in this research is based on the Memotion dataset, which involves categorising memes based on irony, comedy, motivation, and overall-sentiment. Three separate innovative transformer-based techniques have been developed, and their outcomes have been thoroughly reviewed.The best algorithm achieved a macro F1 score of 0.633 for humour classification, 0.55 for motivation classification, 0.61 for sarcasm classification, and 0.575 for overall sentiment of the meme out of all our techniques.

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