Multimodal Analysis of memes for sentiment extraction
This work addresses sentiment analysis in memes for social media applications, but it appears incremental as it builds on existing datasets and methods.
The researchers tackled the problem of analyzing memes for sentiment extraction by developing three transformer-based techniques, achieving macro F1 scores of up to 0.633 for humor classification, 0.55 for motivation, 0.61 for sarcasm, and 0.575 for overall sentiment.
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.