Facets rely on “Subject Verb Object” (SVO) processing of content, with a dash of the Concept Matrix thrown in. SVO processing lets us extract Facets and associated Attributes from sentences like “The service was slow.” In that case there’s a Facet “service” with an Attribute of “slow”. Get enough of these in a set of reviews, and they bubble very nicely to the top. Lexalytics’ Concept Matrix helps us automatically associate like facets, even if they aren’t the same word – rolling them up together.
In the 6 minute video below, I talk about how Facets work, and give an example based on 2 sets of restaurant reviews from Yelp. Facets present a very different view of the data than Yelp gives natively – rather than just seeing the featured reviews and a histogram of review grades, you can actually see which restaurant is hard to get into and which restaurant has a rocking gay bar.