The quality of any text mining technique is highly dependent on the features that are used to
represent the document collection. A classical form of document representation is the vector …

Maintaining large collection of documents is an important problem in many areas of science
and industry. Different analysis can be performed on large document collection with ease …

Topic modeling is an unsupervised learning task that discovers the hidden topics in a
collection of documents. In turn, the discovered topics can be used for summarizing …

Topic modelling methods such as Latent Dirichlet Allocation (LDA) have been successfully
applied to various fields, since these methods can effectively characterize document …

Most text mining methods are based on representing documents using a vector space
model, commonly known as a bag of word model, where each document is modeled as a …

A simple and intuitive way to organize a huge document collection is by a topic hierarchy.
Generally two steps are carried out to build a topic hierarchy automatically: 1) hierarchical …

Topic modeling has been widely utilized in the fields of information retrieval, text mining, text
classification etc. Most existing statistical topic modeling methods such as LDA and pLSA …

This chapter introduces an approach of deriving taxonomy from documents using a novel
document profile model that enables document representations with the semantic …

Many mature term-based or pattern-based approaches have been used in the field of
information filtering to generate users' information needs from a collection of documents. A …

Topic modeling is an effective text mining and information retrieval approach to organizing
knowledge with various contents under a specific topic. Text documents in form of news …