Topic modelling is the new revolution in text mining. It is a statistical technique for revealing
the underlying semantic structure in large collection of documents. After analysing …

Background With the rapid accumulation of biological datasets, machine learning methods
designed to automate data analysis are urgently needed. In recent years, so-called topic …

Topic modeling is one of the most powerful techniques in text mining for data mining, latent
data discovery, and finding relationships among data and text documents. Researchers …

Statistical models of text have become increasingly popular in statistics and computer
science as a method of exploring large document collections. Social scientists often want to …

Abstract Variants of Naive Bayes (NB) and Support Vector Machines (SVM) are often used
as baseline methods for text classification, but their performance varies greatly depending …

Most word embedding models typically represent each word using a single vector, which
makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to …

A significant portion of the world's text is tagged by readers on social bookmarking websites.
Credit attribution is an inherent problem in these corpora because most pages have multiple …

Probabilistic topic models are widely used to discover latent topics in document collections,
while latent feature vector representations of words have been used to obtain high …

Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly
embraced by individuals, groups, and organizations as a valuable source of information …

Topic models have great potential for helping users understand document corpora. This
potential is stymied by their purely unsupervised nature, which often leads to topics that are …