We've had a look at the incredible power of text analysis, and the kind of things we can do with it – as well as the kind of tools we would be using to take advantage of this. Data has become increasingly easy for us to access, and with the growth of social media, we have continuous access to both new data, as well as standardized annotated datasets.
This book will aim at walking the reader through the tools and knowledge required to conduct textual analysis on their own personal data or own standardized datasets. We will discuss methods to access and clean data to make it ready for preprocessing, as well as how to explore and organize our textual data. Classification and clustering are two other commonly conducted text processing tasks, and we will figure out how to perform this as well, before finishing up with how to use deep learning for text.
In the next chapter, we will introduce how and why Python is the right choice for our purposes, as well as discuss some Python tricks and tips to help us with text analysis.
