In this paper, we formally define the problem of topic modeling with network structure (TMN).
We propose a novel solution to this problem, which regularizes a statistical topic model with …

This article studies the problem of latent community topic analysis in text-associated graphs.
With the development of social media, a lot of user-generated content is available with user …

Document networks, ie, networks associated with text information, are becoming
increasingly popular due to the ubiquity of Web documents, blogs, and various kinds of …

Analyzing short texts infers discriminative and coherent latent topics that is a critical and
fundamental task since many real-world applications require semantic understanding of …

Social networks such as Facebook, LinkedIn, and Twitter have been a crucial source of
information for a wide spectrum of users. In Twitter, popular information that is deemed …

The overwhelming amount of short text data on social media and elsewhere has posed
great challenges to topic modeling due to the sparsity problem. Most existing attempts to …

Topic modeling refers to the task of discovering the underlying thematic structure in a text
corpus, where the output is commonly presented as a report of the top terms appearing in …

Nowadays, massive short texts, such as social media posts and newspaper titles, are
available on the Internet. Analyzing these short texts is very significant for many content …

Topic models have been applied to everything from books to newspapers to social media
posts in an effort to identify the most prevalent themes of a text corpus. We provide an in …

Managing large collections of documents is an important problem for many areas of science,
industry, and culture. Probabilistic topic modeling offers a promising solution. Topic …