Suppose you have a list of 100,000+ google queries related to travel bookings. For example:
hotels in london barcelona flight city breaks to berlin khao san road hostel luxury holiday to paris new york business class flight price disneyland trip...
- How can I extract the location i.e., London
- Classify the Line of Business i.e., flight, hotel, package, etc
- Classify the affinity i.e., luxury, family, city break, beach etc
Use this info to record the frequency of various pattern that exists in the keywords
i.e. **keyword pattern** **frequency** (destination) hotel xxx flight to (destination) yyy (theme) (destination) hotel zzz
Manual - get an exhaustive as possible list of locations (most of the queries will be for tourist destinations) and look for a match against the keywords. Again, compare the keywords against possible Line of Business Identifiers and Affinity Identifiers
Google Cloud Natural Language API - This can be used to analyse Entities and Sentiment of text. e.g. hotels in london -> entity(hotels), entity(london) barcelona flight -> entity(flight), entity(barcelona) This isn't very powerful and only supports english.
Machine learning - seems difficult as I haven't got any descriptors for the keywords. Would Naive Bayes be applicable or SVM?
I'd preferably like to run any solution in R too.
Could someone please suggest a direction/potential solution?