In the previous chapter, we saw how to apply ConvNets to images. During this chapter, we will apply similar ideas to texts.
What do a text and an image have in common? At first glance, very little. However, if we represent sentences or documents as a matrix then this matrix is not different from an image matrix where each cell is a pixel. So, the next question is, how can we represent a text as a matrix? Well, it is pretty simple: each row of a matrix is a vector which represents a basic unit of the text. Of course, now we need to define what a basic unit is. A simple choice could be to say that the basic unit is a character. Another choice would be to say that a basic unit is a word, yet another choice is to aggregate similar words together and then denote each aggregation (sometimes called cluster or embedding) with a representative symbol.
