Computer Science > Artificial Intelligence
arXiv:2207.03317 (cs)
[Submitted on 7 Jul 2022]
Title:Multimodal Feature Extraction for Memes Sentiment Classification
View a PDF of the paper titled Multimodal Feature Extraction for Memes Sentiment Classification, by Sofiane Ouaari and 2 other authors
View PDFAbstract:In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. In this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2207.03317 [cs.AI] |
| (orarXiv:2207.03317v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2207.03317 arXiv-issued DOI via DataCite |
Submission history
From: Tsegaye Misikir Tashu [view email][v1] Thu, 7 Jul 2022 14:21:52 UTC (416 KB)
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View a PDF of the paper titled Multimodal Feature Extraction for Memes Sentiment Classification, by Sofiane Ouaari and 2 other authors
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