Products -> AI In-the-Box for Hostel Reviews
AI In-the-Box for Hostel Reviews consists of 130 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 hostel 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? vs. not recommend?
Overall good/satisfied. vs. Disappointed?
Is it recommended for: young people? solo travelers? couples? groups? kids?
Is hostel alright? clean? dirty? renovated? outdated? smelly?
Was it a party hostel? Too noisy? What about the vibe? Was it welcoming? Is there a common room to hang out?
Critical opinions / Alerts for owners
Bed bugs? Theft? Felt unsafe? Dangerous hostel? Dangerous neighbourhood?
Hostel was not as advertised,
Dirty hostel, Awful/Disgusting Hostel?
Reservation problem? Overbooked?
Room - Was it spacious? Was it clean? Does it have amenities? Lockers available? Dorms overcrowded?
Does it have space for personal belongings? Furniture/chairs/table available?
Bathroom - Was it clean? Got a soap? Any shower problems? Shared bathroom was alright? Overcrowded? Small?
Opinions about Location
Opinion In General - is it a good location?
Close to: City centre? Local attractions? Public transportation? Restaurants? Shopping? Airport? Highway? Nature?
Neighbourhood - is it a good neighbourhood? Is it safe?
Staff - Are they friendly? professional? well informed?
Manager - friendly? professional? available?
Check-in - is it smooth and fast or slow?
Cleaning Service - is it good? cleaned daily?
Opinions about Accessibility
Handicap accessible or complains? Bathrooms/Stairs are handicap accessible?
Elevator - Available? Works good or broken?
Does it have parking? Is it ok? Is it big? No problem with finding a space?
Wifi - is it free? Is it fast? Works in the room?
Pool - is it open? Is it clean?
Gym - is it good? Or small and broken?
Pool table / footbal table / ping-pong available?
Laundry available? Pet-friendly/unfriendly?
Problem with payments, only cash payment,
problem with refund
Additional fees for: lockers, sheets/towels, luggage storage?
Was the price adequate? cheap or overpriced? money waste?
Breakfast: Rich, plenty options? Small? Tasty?
Included in price? Not included?
Food price: Was it adequate? Or expensive?
If there will be need, we can expand this list.
First, you process all reviews of a given hostel and extract important information with high accuracy. Then, it is easy to compare these results with other hostels, and valuable patterns emerge.
Our AI deeply analyzed all 4356 of your reviews, compared with 650 000 reviews of similar businesses and extracted 17 Recommendations/Tips how you can improve your business:
Compare your parameters with similar businesses
Here, we focused on showing classified negative opinions critical to hostel owners and presented on a brand reputation dashboard.
By clicking on specific semantic category (here: Noisy rooms) we get specific sentences/segments and "not the whole review" where people mentioned noisy rooms. We detect all the information that is correlated to noisy rooms (e.g. cleaner is banging around), not just with keywords (e.g. noisy, loud). This is the advantage if you use Human-like Analysis (Semantic Analysis) instead of keywords.
It is just a simple comparison made only on 200 reviews and limited categories/areas. You have 130 Semantic Models which can be used to make comparisons in general or in very specific areas.
Hostel Avs. Hostel B
bathroom clean 1 dirty 10
cleaning service good 4 poor 4
bed dirty 1
Positive: 30, Negative: 16
bathroom clean 1 dirty 7
cleaning service good 5 poor 16
bed dirty 6
Positive: 33, Negative: 43
helpful, friendly 94
unhelpful, unfriendly 9
check-in fast 8 slow 1
Positive: 102, Negative: 10
helpful, friendly 57
unhelpful, unfriendly 35
check-in fast 22 slow 8
Positive: 79, Negative: 43
Addons comparison (comparing only negative opinions):
not free 20
parking not free 12 small 1
not free 9
parking not free 16
Clients Opinion comparison:
overall good, satisfied 6
will return 4
will recommend 2
overall disappointed 2
will not return 3
Positive: 12, Negative: 5
overall good, satisfied 4
will return 5
will recommend 4
overall disappointed 14
will not return 7
not recommend 3
Positive: 13, Negative: 24
Hostel A wins (Positive: 144, Negative: 68)
over Hostel B (Positive: 105, Negative: 141).
Search based on your individual preferences matched with opinions of similar people to you and data from reviews
What is important to you? Personalize your search
Our AI remember these parameters while reading millions of reviews and find you the best possible place
Our AI deeply analyzed 3 million reviews from Berlin to match your personal needs:
Meet the Alchemist Traveler. He has been everywhere and he has read all reviews in the world (and he remembers them all).
He is patient and he can spare a moment to plan your perfect vacation.
Instead of reading thousands of reviews individually, you can see a semantic summary and get a feeling for what is contained within them in 20 seconds. You can click for specific information and see extracted sentences from reviews.
Hostelhostelrenovated 21 smelly 19 clean 15 good vibe 9
Roomsroom spacious 27 clean 22 well equiped 15 sth broken 4
Servicestaffhelpful, friendly 23 unhelpful, unfriendly 15 check-in slow 12
Foodbreakfastrich, plenty options 13 not free as promised 9 great 6
Pricehosteloverpriced 23 money waste 15 adequate price 4
Locationclose tonature 7 restaurants 5 public transportation 4
Addonswififree 11 problem 9 slow 7
Otherdeal breakerstheft 4
The automatic summary made from reviews - after processed by AI In-the-Box for Hostel Reviews (no additional NLP/NLU/ML or post-processing needed).
The first no-block title (Hostel, Price etc.) is a Category from our API, blue blocks (Bathroom, Payment etc.) are Aspects, green and red blocks (outdated, well equipped etc.) is the Feature and Polarity (Feature is text, Polarity is color).
See the live demo of semantic summary made from hotel reviews (powered by AI In-the-Box for Hotel Reviews).
All displayed results make sense. By clicking on specific information, you can display actual sentences (not whole reviews).
Our AI deeply analyzed 5422 Reviews and organized them for you
(so you do not have to read them all)
|People was the most satisfied with:|
|Furnitures in Rooms||
|Close to Nature||
|People complained about:|
|Long Waiting line||
|Cash payment only||
|show me more...|
By understanding a review better, you are able to provide deeper and easier-to-understand analysis for your clients.
Positive vs. Negative
Overall good/satisfied vs. Overall dissapointed
Will come back vs. Will not come back
Will recommend vs. Will not recommend
Language has color. Do not reduce it to black & white.
Information extracted by 130 Semantic Models is precise enough to create automatic text summary.
Natural Language Generation Summary from Reviews could look like this:
Our AI deeply analyzed all your reviews for the past 3 months, combined them with reviews of similar businesses and created this summary:
Almost everyone (92%) was satisfied with their stay, the majority of them (85%) would come back and will recommend this hostel (70%).
Reviewers state that rooms in this hostel are clean (34 mentions) and spacious (22 m.) and well-equipped (15 m.). Unfortunately, reviewers also stated that the hostel was outdated (23 m.) and smelly (22 m.). The majority found the staff helpful and friendly (23 m.) but some stated that they were unprofessional (19 m.) and the check-in process (12 m.) was slow.
There were also some issues with the Pool (23m.) and the Gym (15m.)
Generally, people say it is a good value for money (42m.).
|Semantic distribution summary out of text:|
|Would come back||
|Issues with Pool||
|Issues with Gym||
|Good value for money||
Want to talk more about these solutions?
Example hostel review:
Simplified output after processing by AI In-the-Box for Hostel Reviews:
|1.||We stayed in this hostel for one night because it was close to the airport||
|2.||The check in lasts 30mins so we got a little bit angry but it was because there was a lot of people||
|3.||Beside that, everything was fine||
overall good, satisfied +2
|4.||Shared bathrooms were not overcrowded, common room has bilard and ping pong||
not overcrowded +1
common room has bilard +1 has ping-pong +1
|5.||At the end we had some troubles with the payments because they accepted only cash||
cash only -1
|6.||Good value for money||
adequate price +1
|7.||Overall it is a good choice||
overall good, satisfied +2
We specialize in creating dedicated Natural Language Understanding engine for specific reviews. We focused in travel and prepared a set of products to process many different travel reviews (e.g. hotel, hostel, apartment, restaurant, airline reviews).
We stand by the statement that we detect more information than Watson or than any other Deep Learning solution and we are more accurate than Google in Travel Domain. We are also much more cost-effective. And we allow the processing of as much data as you want with no additional cost (0 cents / text).
This is possible because we created the next-generaion process of developing NLU systems in which we can build Semantic Models 10 x faster. It is designed from the ground up to process reviews, not proper english texts. It is the result of 12 years of experience in NLP/NLU and 8 years in processing reviews, and more than 50 big implementations (2007-2019) of NLU systems to process reviews and other user generated content.
We are passionate and proud of our technology and aim to provide the optimal technology for travel reviews. We will be happy to help you implement your brilliant ideas and discover what is possible.
|"Breakfast was tasty"||=>||Breakfast: positive|
|"Breakfast was huge"||=>||Breakfast: positive|
|"Breakfast was not free"||=>||Breakfast: negative|
|"Breakfast was very tasty but really small"||=>||Breakfast: neutral|
|"Breakfast was huge but you had to pay extra"||=>||Breakfast: neutral|
|"Breakfast was tasty"||=>||Breakfast: tasty|
|"Breakfast was huge"||=>||Breakfast: big portions|
|"Breakfast was not free"||=>||Breakfast: free|
|"Breakfast was very tasty but really small"||=>||Breakfast: tasty | small portions|
|"Breakfast was huge but you had to pay extra"||=>||Breakfast: big portions | not free|
Language has colors. Do not reduce it to Black & White.
Technological differences (compared to other NLP/NLU solutions):
The semantic model is the newest approach to an information extraction models where boundaries of those models are not determined by the structure of a language (or grammar, or used words), but the type of information you are detecting. It does not matter how the user writes an opinion about a specific object, e.g. “breakfast was a joke”, “Muffin and cold coffee is not a breakfast”, “Breakfast wasn’t included as written”, our semantic model still detects it and provides structured-data. With "breakfast" example, it is relatively easy to provide a shallow analysis (e.g. Sentiment Analysis) with the current NLP techniques. If you are looking for more sophisticated information (e.g. is it safe?, is it handicap friendly?, will someone come back?, Is it sustainable? Is it pet-friendly?) the situation gets more complicated. Current techniques provide a part of this information and their accuracy relies on the keywords used in reviews. Users are very creative in the reviews and they are not using a keyword approach. Let’s take one example. If we want to answer the question “is it safe?”, you need to capture a lot of information from reviews without common keywords safe/dangerous. In practice they write this in tens or hundreds of ways: e.g. “there was a lot of drunken people outside”, “I did not feel good because of the neighbors”, “someone yelled in the night and the front desk lady did nothing”. We believe, that if the human can understand what is written in the review, then technology should detect it as well. That is why we call them Semantic Models, not information extraction models.
Our UnicornNLP Semantic Environment is a place where we design and precisely craft all the semantic models. It is a perfect combination of human and computer intelligence. It is the secret sauce of our technology and it allows us to deliver the best technology in the world for reviews. Statistical algorithms provide the data based on the processing of hundreds of thousands of reviews, but the humans are making all the crucial decisions. Given the pace of AI development, this is significant advantage in information extraction (in NLP/NLU) and it will be appropriate for the next decade. Think about our environment as a way of creating tens of thousands of very precise, hand-crafted semantic rules but with an AI as an assistant that is generating new ideas. These ideas are provided by an AI assistant based on constant simulations on real data and statistical tools.
We designed a semantic programming language (QL4Reviews) to extract information from reviews. With this programming language, you build semantic models 10 times faster than in other current technologies. Every Semantic Model is programmed in QL4Reviews and consist of 50-2000 lines of semantic code. Heavily optimized engine allow to process through 124 Semantic Models on 1 low-cpu Amazon AWS instance - 1 million reviews a day. Speed of the core engine was very important for us from the beginning.
Our technology is designed from the ground up to process reviews, not proper-grammar texts. It can process also machine-translated reviews/texts (via Google Translate or Microsoft Translate). This approach provides almost as good results as processing English reviews but we do not have to rewrite deep semantic models into other languages. It is much easier to maintain and scale (because there is only 1 engine).
Read more & See demo: Processing machine-translated non-English Travel Reviews
This is not just another NLP API/Tool. We redesigned every layer of technology and we wanted to address the biggest challenge in the NLP/NLU industry. We wanted to change the way you use and think about Natural Language Understanding API. In our opinion, if you are using NLP API, you should not get your hands dirty and spend inordinate time thinking how to best implement this API into your application. We wanted to hide all the NLP complexity inside the system and provide you ready-to-use data. We believe that NLP API should be simple and easy to use without linguistic, machine learning or other expertise. You do not need to spend weeks to improve the accuracy of "domain-indepentent" NLP API, to adjust to Your domain or to make decisions about complex output parameters. Getting output parameters like confidency (e.g. 0.7852) or polarity (e.g. 0.4352) or keywords detected (e.g. friendly, 0.6786; dissapointed, -0.7564) gives every developer problems and frankly they are not standard or predictable. They are rather a consequence of used technology inside, and putting these parameters for the output in our opinion is the way to leave configuration and responsibilty for decision-making to You. We believe in human-like output where a human can understand what this API detected. We believe that we have developed excellent API for specific domains, so you can get tailored results and focus on your business. We position ourselfes as a read-to-use technology, not a standard API that is a tool that you need to configure and train to your data.
If you want to buy a Product, receive more info about the On-Premise version (AI In-the-Box) and Plans&Pricing
or want to discuss what is possible
If you want to test it or see how it works on your data, send us a dataset
(we do not collect your data)
You can also start with the SaaS version - pay per text (hosted via rapidAPI & Amazon AWS)
before you get the On-Premise version with 0 cents / text!