Naive Bayes Classifiers are a family of algorithms that are based on Bayes theorem. They are classification algorithms with the main principle being that every feature being classified is independent of the other. P(c) Prior, number of documents in class c divided by the total number of documents. We have the following documents. Document 1 and 2 are Burgers while 3 and 4 are Sandwich.

Large amounts of data are collected everyday. As more information becomes available, it becomes difficult to access what we are looking for. So, we need tools and techniques to organize, search and understand vast quantities of information. Topic modelling provides us with methods to organize, understand and summarize large collections of textual information. Topic modelling can be described as a method for finding a group of words (i.e topic) from a collection of documents that best represents the information in the collection. It can also be thought of as a form of text mining – a way to obtain recurring patterns of words in textual material.

Large amounts of data are collected everyday. As more information becomes available, it becomes difficult to access what we are looking for. So, we need tools and techniques to organize, search and understand vast quantities of information. Topic modelling provides us with methods to organize, understand and summarize large collections of textual information. Topic modelling can be described as a method for finding a group of words (i.e topic) from a collection of documents that best represents the information in the collection. It can also be thought of as a form of text mining – a way to obtain recurring patterns of words in textual material.