By Tom Krupa, On 11th November 2020
We are happy to announce that Semantic Analysis for Apartment Reviews is publicly available in RapidAPI (link).
What is a Semantic Analysis for Apartment Reviews?
It is a Language Understanding API consisting of 149 language understanding models trained&tested on apartment, vacation rental, home rental and other accommodation reviews.
For more information, visit our product site: http://unicornnlp.com/?Semantic-Analysis-for-Apartment-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-apartment-reviews
Example apartment review:
Simplified output after processing by Semantic Analysis for Apartment Reviews:
|1.||We got this apartment because of its cheap price and near to the centre||
city center +1
apartment inexpensive +1
|2.||The apartment was a little bit small and the bed was not that comfortable||
bed uncomfy -2
|3.||There was a lot of lovely decorations and it was nice||
|4.||There was very fast wifi, which was great||
works good +2
|5.||However there was no parking spaces in front of the house so we spend some time sechrching for a place||
lack of -1
|6.||Also the neighborhood was full of drunken people, probably it was because the house was not that far from downtown and beach and there was a lot of homeless people||
not safe -2
close to city center +1
close to beach +1
|7.||Will not come back||
will not return -2
Semantic Analysis for Vacation Rental / Apartment Reviews consists of 142 dedicated Semantic Models. Each Semantic model was especially designed, built, tested, and re-tested on hundreds of thousands of vacation rental, apartment and other 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 apartment reviews.
In other words, what type of information do we garner from reviews.
Client loyalty & Recommendations
Will he come back? vs. not come back?
Will he recommend it? vs. not recommend?
Overall good/satisfied. vs. disappointed?
Is it recommended for: Couples? Family with kids?
How was the apartment in general? Clean? Dirty? Renovated? Outdated? Were stairs too steep?
Was everything as advertised?
Garden/Terrace available/not available?
Critical opinions / Alerts for the owners
Bed bugs? Theft? Felt secure?
Apartment was not as advertised?
Dirty apartment? Does the host respect privacy?
Rooms - Spacious? Clean? Did everything work fine?
Bed - Comfortable? Uncomfortable?
Living Room: Ok? Big? Tiny?
Was there adequate sleeping spaces?
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?
Kitchen: Amenities available/not available?
Bathroom: Was it clean? Spacious or small? Has good amenities? Was it shared or private?
Shower was alright? Is pressure good?
Handicap accessible or complaints?
Bathrooms/Stairs are handicap accessible?
Were keys at the location? Do they have a keypad entry?
Does it have parking? Is it ok? Is it big? Is it easy to find a space?
Was the host polite? Friendly? Well informed? Did he respond quickly? Did he respect privacy? Was he at the location? Does he have a pet? Or maybe lack of any interactions?
Problem with payments?
Problem with refund?
Additional fees? Is a deposit necessary?
Was it expensive? Cheap? Was price adequate or overpriced?
Detergents available/not available?
Laundry machine available/not available?
Washing machine available/not available?
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