The sentimental rankings
In the first use case, we explored the venue data from Foursquare and built some analysis and a proper solution on top of that data. In this section, we will focus on the textual aspect of the Foursquare data. We will extract the tips generated for a venue by users and perform some basic analysis on them. Then we will try to build a use case in which we will use those tips to arrive at a decision.
Extracting tips data – the go to step
By now we know the analysis work flow off by heart and as always the first step is getting to the required data. We have already detailed the steps involved in data extraction with Foursquare APIs. So instead of restating the obvious, we will start with the process of data extraction.
We have written two utility functions for the extraction of tips data from the identified end point:
extract_all_tips_by_venue: This function takes the ID of the venue as an argument and extracts the JSON object containing all the tips for that venue...
