I discovered the Natural language toolkit working through Python tutorials.

Working with specific issues in language analysis, the NLTK‌ library had everything needed to analyse groups of words in sections of text. It was then possible to parse groups of words that were a description in 4,5,6,etc ‘n-grams’. For example, coherent sentence sections to be validated against real-world data.

Comparing end results against known data for validation showed the success rate is high. Therefore, this makes the library (which has many other features!) very powerful for relating and categorising textual data. To sum up, the NLTK is essential for data refinement.

Personal experience

After a short learning curve, Python is incredibly simple to pick up, and the libraries are easy to access. There are many tutorials on sites like Udemy to help with learning it.

Use cases

You can analyse large amounts of language for naturally occurring instances of understandable phrases.

Cons

To go deeper, you’ll need knowledge of all the types of language classifications to filter the results effectively.