If we were to comprehend programmatically everything a person speaks in a day, we would definitely end up with quite a bit of information, but how about the composition of these sentences — the words?
This is where the real challenge lies. Computers process numbers with ease, but any text fed into it as such, only leads to increased storage consumption without the computation of useful information.
Using NLP, we build algorithms that can explore and understand i.e., work seamlessly with such categories of data. …
Picture this, we need to make a classification system for an e-book platform, with sociological and scientific research!
The titles, content, and respective authors are known to us. How do we proceed from here? Should we really screen the whole article, only to draw similarities/differences?
Here’s an easy way out — Topic Modeling!
Much like the word suggests, our goal is to find underlying topics that organize this collection. Topic Modeling is an unsupervised class of Machine Learning techniques, which means we don’t require a set of target values for its implementation.
LDA (Latent Dirichlet Allocation) is the most popular…