Topic Detecting on Multimodal News Data Based on Deep Learning
[Objective]This paper constructs a multimodal topic model combining text and images in news based on multimodal learning methods.It aims to uncover latent topics in the news automatically.[Methods]We constructed a model incorporating word embedding for topics from texts and images.It uses multimodal joint representation learning and coordinate representation learning for feature fusion.We conducted visual analysis for the discovered multimodal news topics.Finally,we examined the new model on the N15News dataset.[Results]Compared to Label-ETM using only text features,the multimodal topic modeling approach can achieve better topic interpretability and diversity.This suggests that the multimodal topic modeling approach is feasible.[Limitations]We assume images and text in news are semantically and thematically related.Multimodal fusion methods need to be improved in weakly related and irrelevant domains.[Conclusions]Multimodal topic modeling can discover connections between different modal data and improve the diversity of discovered topics.
Topic ModelMulti-Modal Joint RepresentationMulti-Modal Coordinate RepresentationTopic Detecting for News
NETL
NSTL
万方数据
