Topic models have been prevalent for decades to discover latent topics and infer topic
proportions of documents in an unsupervised fashion. They have been widely used in …

Topic modelling has been a successful technique for text analysis for almost twenty years.
When topic modelling met deep neural networks, there emerged a new and increasingly …

The rise of social media platforms provides an unbounded, infinitely rich source of
aggregate knowledge of the world around us, both historic and real-time, from a human …

The growing societal dependence on social media and user generated content for news and
information has increased the influence of unreliable sources and fake content, which …

Guided Diverse Concept Miner (GDCM) is an interpretable deep learning algorithm to (1)
automatically extract corpus-level concepts from text data,(2) focus the discovery of concepts …

With the emergence and development of deep generative models, such as the variational
auto-encoders (VAEs), the research on topic modeling successfully extends to a new area …

With the rapid proliferation of social networking sites (SNS), automatic topic extraction from
various text messages posted on SNS are becoming an important source of information for …

Owing to the widespread use of smartphones and mobile devices and the prevalence of
image-sharing social network services, the amount of image data available on the Web is …

Topic modeling methods such as latent Dirichlet allocation (LDA) are powerful tools for
analyzing massive amounts of textual data. They have been used extensively in information …

Extracting useful knowledge from multimodal data is the core of many multimedia
applications, such as recommendation systems, and cross-modal retrieval. In this paper, we …