Topic modeling using Latent Dirichlet Allocation
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. This technique can be used to uncover hidden thematic structure in a given set of documents.
Topic modeling helps us to organize documents in an optimal way, which can then be used for analysis. One thing to note about topic modeling algorithms is that they don't need labeled data. It is like unsupervised learning in that it will identify the patterns on its own. Given the enormous volumes of text data generated on the internet, topic modeling is important because it enables the summarization of vast amounts of data, which would otherwise not be possible.
Latent Dirichlet Allocation is a topic modeling technique, the underlying concept of which is that a given piece of text is a combination of multiple...
