Using AI to calculate a fair review score for a venue
In this post, we explain how we use some AI and old-fashioned content scraping to generate a fair review score for a venue. We call this "FairScore" and it makes up part of our product, Barkeeper
The full version of this post is available on the Nekotachi Blog.
In this post, we explain how we use some AI and old-fashioned content scraping to generate a fair review score for a venue.
We call this  "FairScore" and it makes up part of our product, Barkeeper
Content sources
Content is very important for building the FairScore. It relies heavily on reviews posted about a venue on the following services:
- Google Places / Google Maps
- Yelp
- TripAdvisor
Where an API is available to get access to review content, it uses that. Otherwise, the content is scraped and processed. The data from all the sources is cleaned up and stored in a standard schema to make comparisons easier.
Reviews which are 1 star but have no content are ignored. Reviews which are overly negative but have a reply from the owner refuting the claim (or helping the customer) are also ignored.
Additionally, FairScore also checks social media posts for entries about the particular venue that express an opinion about it.
FairScores can change between calculations, since they are partly based on data sources that are updating in realtime.
FairScore also doesn't mind what the language a particular piece of content is in. It's designed to handle multiple languages, like all of the other services I've built for Barkeeper.
Want to learn more?
You can read the rest of this post over on Nekotachi Ltd's blog.