Python Natural Language Processing

Application

In this section, we will look at some cool applications that use TF-IDF:

• In general, text data analysis can be performed by TF-IDF easily. You can get information about the most accurate keywords for your dataset.
• If you are developing a text summarization application where you have a selected statistical approach, then TF-IDF is the most important feature for generating a summary for the document.
• Variations of the TF-IDF weighting scheme are often used by search engines to find out the scoring and ranking of a document's relevance for a given user query.
• Document classification applications use this technique along with BOW.

Now let's look at the concept of vectorization for an NLP application.