Natural Language Processing (NLP) is a way for computers to understand human language. One of the cool things about NLP is how it can use something called unsupervised learning for topic modeling. Let’s break this down into simpler parts:

1. Understanding Data:

   • In unsupervised learning, we don’t need labeled data. This means we can use lots of text documents without needing to tag them first.
   • The computer looks through the data and finds patterns on its own. This helps us see how the information is organized.

2. Common Techniques:

   • Latent Dirichlet Allocation (LDA): This is a well-known method for figuring out topics in text. It groups words that often appear together and helps assign them to different topics. You just tell it how many topics you want, and it does the rest.
   • Non-negative Matrix Factorization (NMF): This is another method that breaks down the text data into parts that we can understand better. It helps to see what topics are present without using any negative values.

3. Practical Applications:

   • These methods are really useful for:
     • Content Summarization: They can quickly sum up large amounts of text.
     • Recommendation Systems: They can group similar topics or items, which helps suggest related content that users might like.

By using these unsupervised learning techniques, we can find hidden insights in text without needing to label everything ourselves.