By Tom Krupa, On 9th September 2020
We are happy to announce that Semantic Analysis for Hotel Reviews is publicly available on RapidAPI (link).
What is a Semantic Analysis for Hotel Reviews?
It is a Language Understanding API consisting of 149 language understanding models trained&tested on Hotel Reviews, B&B Reviews, and other accommodation reviews.
For more information, visit our product site: http://unicornnlp.com/?Semantic-Analysis-for-Hotel-Reviews
API Documentation: http://unicornnlp.com/?API-Documentation-Semantic-Analysis-Reviews
RapidAPI Link (use this link to connect to the API): https://rapidapi.com/unicornNLP/api/semantic-analysis-for-hotel-reviews
Example hotel review:
Simplified output after processing by Semantic Analysis for Hotel Reviews:
|1.||The hotel was clean and renovated, service was friendly too||
staff helpful, friendly +2
hotel renovated +1
|2.||But thats it|
|3.||The bathroom was dirty, Shower got poor pressure||
shower problem -2
bathroom dirty -2
|4.||I think there were fleas in our bed||
|5.||The pool was closed during our stay and staff couldn't tell us where it will be open and does not seem to care||
staff unfriendly, unhelpful -2
|6.||The breakfast was a joke, the portions was small and we were told that we could pay to get more, so it wasn't free as promised||
poor, limited -1
breakfast not free as promised -2
breakfast bad -1
|7.||Wanted to get my money back but they refused, not coming back||
will not return -2
payment refound problem -1
Semantic Analysis for Hotel Reviews consists of 149 Semantic Models. Each Semantic model was especially designed, built, tested, and re-tested on hundreds of thousands of accommodation reviews from 10 different sources. All presented Semantic Models work with an unparalleled precision of 90-95%.
Below, you will find a high-level view of all semantic models designed for hotel reviews.
In other words, what type of information do we garner from reviews.
Will he come back? vs. not come back?
Will he recommend it? vs. not recommend?
Was he satisfied. vs. disappointed?
How was the hotel in general? Was it clean? Dirty?
Was it nice? Smelly? Renovated? Outdated?
Was a visit satisfying? Is this hotel a hidden gem?
Critical opinions / Alerts for the owners
Bed bugs? Theft? Felt secure?
The hotel was not as advertised? Misleading info?
Dirty hotel? Is service unpleasant/unprofessional? Didn't care?
Contact problem with the Manager? Problem with refund?
Room - Was it spacious? Was it clean? Was it comfy?
Was it outdated? Renovated? Does it have amenities?
Bathroom - Was it clean? Soap provided? Poor shower pressure?
Kitchen amenities - Is there a fridge, coffee maker, cups?
Opinion In General - Is it a good location?
Close to: City center? Public transportation? Restaurants? Shopping? Airport? Highway? Nature?
Neighborhood: Is it a good neighborhood? Is it safe?
Staff - Friendly? Professional?
Manager - Friendly? Professional? Available?
Check-in - Is it smooth, fast or slow?
Cleaning service - Good? Friendly? Professional?
Handicap accessible or complaints?
Elevator - Available? Fast? Working fine or broken?
Bus Shuttle - Is it available? Is it free?
Parking - Is it ok? Big? Small? Is it free?
Valet parking - Available? Professional?
WiFi - Is it free? Is it fast? Works in the room?
Pool - Is it open? Is it clean? Big? Small?
Gym - Is it well-equipped? Big? Small?
TV - Is it new? Big? A lot of tv channels?
Pet - Friendly/Unfriendly?
Problem with payments? Problem with a credit card?
Problem with refund? Problem with the bill?
Hotel charged additional fees? Hidden fees?
Breakfast - Varied and abundant? Or maybe small and limited?
Tasty? Free as promised? Did you have to pay extra?
Food in restaurant- Is it an adequate price? Is it expensive?
You can start using this API today.
If you want to get custom-made private API with different models, or receive more info about the custom On-Premise version with 0 cents/text