Topic modeling is the process of identifying patterns in text data that correspond to a topic. If the text contains multiple topics, then this technique can be used to identify and separate those themes within the input text. We do this to uncover hidden thematic structure in the given set of documents.

Topic modeling helps us to organize our documents in an optimal way, which can then be used for analysis. One thing to note about topic modeling algorithms is that we don't need any labeled data. It is like unsupervised learning where it will identify the patterns on its own. Given the enormous volumes of text data generated on the Internet, topic modeling becomes very important because it enables us to summarize all this data, which would otherwise not be possible.

Latent Dirichlet Allocation is a topic modeling technique where the underlying intuition is that a given piece of text is a combination of multiple topics. Let's consider the following...