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 …

Close computing paradigms such as fog and edge have become promising technologies for
mobile applications running on pervasive mobile equipment utilized by a wide range of …

Social media is becoming popular for news consumption due to its fast dissemination, easy
access, and low cost. However, it also enables the wide propagation of fake news, ie, news …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Document clustering is a gathering of textual content documents into groups or clusters. The
main aim is to cluster the documents, which are internally logical but considerably different …

Principal components analysis (PCA) is a well-known technique for approximating a tabular
data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets …

Topic modeling is a popular technique for exploring large document collections. It has
proven useful for this task, but its application poses a number of challenges. First, the …

Matrix factorization techniques have been frequently applied in information retrieval,
computer vision, and pattern recognition. Among them, Nonnegative Matrix Factorization …

Social media for news consumption is becoming popular nowadays. The low cost, easy
access and rapid information dissemination of social media bring benefits for people to seek …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …