Topic modeling

Topic models can be used for discovering the underlying themes or topics that are present in an unstructured collection of documents. The collection of documents can be organized based on the discovered topics, so that users can easily browse through the documents based on topics of their interest. There are various topic modeling algorithms that can be applied to a collection of documents to achieve this. Clustering is a very useful technique used to group documents, but this doesn't always fit the requirements. When we cluster a text document, the results in each text exclusively belong to exactly one cluster. Let's consider this scenario: We have a book called Text Mining with R Programming Language. Should this book be grouped with R programming-related books, or with text mining-related books? The book is about R programming as well as text mining, and thus should be listed in both sections. In this topic, we will learn methods that do not cluster documents into completely...