论文标题
模因情感分类的多模式特征提取
Multimodal Feature Extraction for Memes Sentiment Classification
论文作者
论文摘要
在这项研究中，我们提出了使用深度学习方法进行多模式模因分类的特征提取。模因通常是一张照片或视频，其中年轻一代在社交媒体平台上分享的文字表达了与文化相关的想法。由于它们是表达情感和感受的有效方法，因此可以对模因背后的情绪进行分类的好分类器很重要。为了使学习过程更有效，请减少过度拟合的可能性，并提高模型的普遍性，需要一种良好的方法来从各种方式中提取共同特征。在这项工作中，我们建议使用不同的多模式神经网络方法进行多模式特征提取，并使用提取的功能训练分类器以识别模因中的情感。
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.
