In our first code example, we will use the multi-domain sentiment dataset (version 2.0)[11]. This dataset contains Amazon reviews from four different product categories. We will download it, preprocess it, and load it into Gota, a data wrangling library, to find the most common phrases in positive and negative reviews that do not co-occur in both. This is a basic example that involves no ML algorithms, but will serve as a hands-on introduction to Go, gophernotes, and Gota.
You can find the full code example in the companion repository to this book at https://github.com/PacktPublishing/Machine-Learning-with-Go-Quick-Start-Guide.
