Topic A

A review of text mining approaches and their function in discovering and extracting a topic

Background and aim: Four text mining methods are examined and focused on understanding and identifying their properties and limitations in subject discovery. Methodology: The study is an analytical review of the literature of text mining and topic modeling.  Findings: LSA could be used to classify specific and unique topics in documents that address only a single topic. The other three text min...

Topic Segmentation with a Structured Topic Model

We present a new hierarchical Bayesian model for unsupervised topic segmentation. This new model integrates a point-wise boundary sampling algorithm used in Bayesian segmentation into a structured topic model that can capture a simple hierarchical topic structure latent in documents. We develop an MCMC inference algorithm to split/merge segment(s). Experimental results show that our model outpe...

Toilet in Transportation Vehicles: A Topic for Consideration on Travel Sanitation in Travel Medicine

Topic Modeling and Classification of Cyberspace Papers Using Text Mining

The global cyberspace networks provide individuals with platforms to can interact, exchange ideas, share information, provide social support, conduct business, create artistic media, play games, engage in political discussions, and many more. The term cyberspace has become a conventional means to describe anything associated with the Internet and the diverse Internet culture. In fact, cyberspac...

VODUM: A Topic Model Unifying Viewpoint, Topic and Opinion Discovery

The surge of opinionated on-line texts provides a wealth of information that can be exploited to analyze users’ viewpoints and opinions on various topics. This article presents VODUM, an unsupervised Topic Model designed to jointly discover viewpoints, topics, and opinions in text. We hypothesize that partitioning topical words and viewpointspecific opinion words using part-of-speech helps to d...