Topic Modeling Open Source Tool

Topic modeling methods and algorithms are different from the use of rule-based text mining which uses keywords in a dictionary or regular expressions in search techniques but rather an unsupervised approach to finding and observing a bunch of words known as topics in large clusters of text. These bunch of words (topics) and patterns are hidden in across document but get discovered and noticed after training a topic model on it. The assumptions of topic models are that each document consist of a mixture of topics and each topic consists of a collection of words. There are many algorithms and methods for building and training a topic model of which some are Latent Semantic Analysis or Indexing (LSA), Probabilistic Latent Semantic Analysts (PLSA), Latent Dirichlet Allocation (LSA), Hierarchical Dirichlet Process (HDP) and others. Among all these, the most common and popular is LDA which is also the first algorithm that has been implemented in this open-source tool.