With the ever-growing volumes of data, machine learning methods designed to automate data analysis are indispensable.
Topic Modelling and Cluster Analysis are two approaches to text-mining. A topic model is used to statistically explore abstract concepts (topics) that occur within a set of documents. Cluster analysis uses perceived relation between various groups of objects to create new sub-groups (clusters). These documents are ideal to submit for Machine Learning.
ZyLAB’s solutions use Topic Modeling to automatically generate an overview of the most used concepts or topics in a text collection. These algorithms automatically find the most dominant topics in a document set and use for each topic the best words to describe them. This works completely un-supervised. By clustering and visualizing the found topics in a hierarchical tree or by using an interactive Word Wheel, you get immediately a clear overview of the dominant topics.
Topic modeling is a useful method that enhances the users’ ability to interpret large volumes of information. With these techniques, you can actually find relevant documents even if you did not really know what words or topics to look for.