A super-fast machine learning model for finding user search intent

n April 2019, Benjamin Burkholder (who is awesome, by the way) published a Medium article showing off a script he wrote that uses SERP result features to infer a user’s search intent. The script uses the SerpAPI.com API for its data and labels search queries in the following way:

Informational — The person is looking for more information on a topic. This is indicated by whether an answer box or PAA (people also ask) boxes are present.
Navigational — The person is searching for a specific website. This is indicated by whether a knowledge graph is present or if site links are present.
Transactional — The person is aiming to purchase something. This is indicated by whether shopping ads are present.
Commercial Investigation — The person is aiming to make a purchase soon but is still researching. This is indicated by whether paid ads are present, an answer box is present, PAAs are present, or if there are ads present at the bottom of the SERP.

This is one of the coolest ways to estimate search intent, because it uses Google’s understanding of search intent (as expressed by the SERP features shown for that search)...

Read more here: A super-fast machine learning model for finding user search intent.