In the japanese anime industry, predicting whether an upcoming product will
be popular is crucial. This paper presents a dataset and methods on predicting
anime popularity using a multimodal textimage dataset constructed exclusively
from freely available internet sources. The dataset was built following
rigorous standards based on real-life investment experiences. A deep neural
network architecture leveraging GPT-2 and ResNet-50 to embed the data was
employed to investigate the correlation between the multimodal text-image input
and a popularity score, discovering relevant strengths and weaknesses in the
dataset. To measure the accuracy of the model, mean squared error (MSE) was
used, obtaining a best result of 0.011 when considering all inputs and the full
version of the deep neural network, compared to the benchmark MSE 0.412
obtained with traditional TF-IDF and PILtotensor vectorizations. This is the
first proposal to address such task with multimodal datasets, revealing the
substantial benefit of incorporating image information, even when a relatively
small model (ResNet-50) was used to embed them.

本研究提出了使用多模态文本图片数据集预测日本动漫热门度的方法，通过使用从互联网中获取的开源数据构建这一数据集，利用基于 GPT-2 和 ResNet-50 的深度神经网络模型，研究多模态文本图片输入与热门度之间的相关性，发现数据集的相关优势和弱点。通过使用均方误差（MSE）度量模型的准确性，当考虑所有输入和完整版本的深度神经网络时，获得最佳结果为 0.011，而传统的 TF-IDF 和 PILtotensor 向量化方法获得的基准 MSE 为 0.412。这是首次使用多模态数据集来解决此任务，揭示了即使使用相对较小的模型（ResNet-50）对图像进行嵌入处理也能带来的显著好处。