Using Deep Learning for End to End Multiclass Text Classification

Have you ever thought about how toxic comments get flagged automatically on platforms like Quora or Reddit? Or how mail gets marked as spam? Or what decides which online ads are shown to you?

All of the above are examples of how text classification is used in different areas. Text classification is a common task in natural language processing (NLP) which transforms a sequence of a text of indefinite length into a single category.

One theme that emerges from the above examples is that all have a binary target class. For example, either the comment is toxic or not toxic, or the review is fake or not fake. In short, there are only two target classes, hence the term binary.

But this is not always the case, and some problems might have more than two target classes. These problems are conveniently termed multiclass classifications, and it is these problems we’ll focus on in this post. Some examples of multiclass classification include:

• The sentiment of a review: positive, negative or neutral (t…