Anime Popularity Prediction Before Huge Investments: a Multimodal Approach Using Deep Learning
<|reference_start|>Anime Popularity Prediction Before Huge Investments: a Multimodal Approach Using Deep Learning: 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.<|reference_end|>
@article{armenta-segura2024anime,
    title={Anime Popularity Prediction Before Huge Investments: a Multimodal
  Approach Using Deep Learning},
    author={Jes'us Armenta-Segura, Grigori Sidorov},
    journal={arXiv preprint arXiv:2406.16961},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16961},
    primaryClass={cs.LG cs.AI}
}